IG_Theme Modelling_final.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "f3708025-ec3f-42e3-ab2e-275b917c7691",
   "metadata": {},
   "source": [
    "# Домашнее задание к занятию «Тематическое моделирование»"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4a9614-e967-4317-8667-0ebcbb96a6a7",
   "metadata": {},
   "source": [
    "### Цель: \n",
    "Обобщить и научиться применять полученные знания в NLP для решения задач классификации текста\n",
    "### Задание:  \n",
    "Классифицировать по тональности отзывы на банки с сайта banki.ru\n",
    "Данные содержат непосредственно тексты отзывов, некоторую дополнительную информацию, а также оценку по шкале от 1 до 5\n",
    "1. Анализ текста  \n",
    "1.1. Посчитайте количество отзывов в разных городах и на разные банки  \n",
    "1.2. Постройте гистограмы длин ~~слов~~ **(текстов ???)** в символах и в словах  \n",
    "1.3. Найдите 10 самых частых: слов, слов без стоп-слов, лемм, существительных  \n",
    "1.4. Постройте кривые Ципфа и Хипса  \n",
    "1.5. Ответьте на вопросы: какое слово встречается чаще, \"сотрудник\" или \"клиент\", сколько раз встречается слова \"мошенничество\" и \"доверие\"?  \n",
    "1.6. В поле \"rating_grade\" записана оценка отзыва по шкале от 1 до 5. Используйте меру $tf-idf$, для того, чтобы найти ключевые слова и биграмы для положительных отзывов (с оценкой 5) и отрицательных отзывов (с оценкой 1)  \n",
    "2. Тематическое моделирование  \n",
    "2.1. Постройте несколько тематических моделей коллекции документов с разным числом тем  \n",
    "2.2. Приведите примеры понятных (интерпретируемых) тем  \n",
    "2.3. Найдите темы, в которых упомянуты конкретные банки (Сбербанк, ВТБ, другой банк). Можете ли вы их прокомментировать /\n",
    "объяснить?  \n",
    "3. Классификация текстов  \n",
    "Будем классифицировать на два класса, различать резко отрицательные отзывы (с оценкой 1) и положительные отзывы (с оценкой 5)  \n",
    "3.1. Составьте обучающее и тестовое множество: выберите из всего набора данных N1 отзывов с оценкой 1 и N2 отзывов с оценкой 5. Используйте sklearn.model_selection.train_test_split для разделения множества отобранных документов на обучающее и тестовое  \n",
    "3.2. Используйте любой известный вам алгоритм классификации текстов для решения задачи и получите baseline.   \n",
    "3.3. Сравните разные варианты векторизации текста: использование только униграм, пар или троек слов или с использованием \n",
    "символьных $n$-грам.  \n",
    "3.4. Сравните, как изменяется качество решения задачи при использовании скрытых тем в качестве признаков:  \n",
    "1-ый вариант: $tf-idf$ преобразование (sklearn.feature_extraction.text.TfidfTransformer) и сингулярное разложение (оно же – латентый семантический анализ) (sklearn.decomposition.TruncatedSVD),  \n",
    "2-ой вариант: тематические модели LDA (sklearn.decomposition.LatentDirichletAllocation).  \n",
    "3.5. Используйте accuracy и F-measure для оценки качества классификации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74392075-f6ba-4ff3-a691-1ae6c758919b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d318f9e1-8e6f-478f-83a2-52cde32fa2cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import bz2\n",
    "import regex\n",
    "from tqdm import tqdm\n",
    "from scipy import sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b59a727c-087f-4b07-93f9-a18d6dc84566",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eae5d0a3-28f1-413d-be49-f3aecf03c26c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\grebe\\\\Documents\\\\EDUCATION\\\\Машинное обучение фундаментальные инструменты и практики\\\\Обработка естественного языка\\\\Тематическое моделирование'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3474da7-e456-4b37-95b6-1b31bc3a62e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#responses = []\n",
    "#with bz2.BZ2File('banki_responses.json.bz2', 'r') as thefile:\n",
    "#    for row in tqdm(thefile):\n",
    "#        resp = json.loads(row)\n",
    "#        if not resp['rating_not_checked'] and (len(resp['text'].split()) > 0):\n",
    "#            responses.append(resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "431b3f7a-a1a1-464d-999d-7995079e2016",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "201030it [00:13, 14705.86it/s]\n"
     ]
    }
   ],
   "source": [
    "# if the file is not archived\n",
    "responses = []\n",
    "with open('banki_responses.json', 'r') as thefile:\n",
    "    for row in tqdm(thefile):\n",
    "        resp = json.loads(row)\n",
    "        if not resp['rating_not_checked'] and (len(resp['text'].split()) > 0):\n",
    "            responses.append(resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e398dbb8-ab06-4ec1-94af-84e19aa92b93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'city': 'г. Москва',\n",
       " 'rating_not_checked': False,\n",
       " 'title': 'Не тут-то было...',\n",
       " 'num_comments': 0,\n",
       " 'bank_license': 'лицензия № 606',\n",
       " 'author': 'bankir76',\n",
       " 'bank_name': 'Гагаринский',\n",
       " 'datetime': '2015-06-05 11:37:31',\n",
       " 'text': 'Я, как и многие вкладчики открыл на 1000 руб. со счастливой мыслью пополнить в последствии... Как говорится не тут-то было.  8% за внесение и никакого нарушения договора (сразу вспоминается: \"ловкость рук ...\". Совершенно непонятно куда в такой ситуации смотрит ЦБ! Ну и абстрактно предлагаю представить следующее: взяв кредит в этом месте (банк здесь слово неуместное) в январе, процентов под 40, вы приходите сегодня вносить очередной платеж и просите добавить вам к вашей сумме процентов эдак 20, ну действительно, почему нет, ситуация-то изменилась...',\n",
       " 'rating_grade': None}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "responses[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7481adf-a6f2-4854-8e27-0af276b7e65d",
   "metadata": {},
   "source": [
    "### 1. Анализ текста"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21c462ec-0830-4204-b58b-47c41c7007cf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(responses, columns = ['city', 'bank_name', 'rating_grade', 'text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae0b6140-9f3b-4ba3-803e-ddc20c62b822",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>bank_name</th>\n",
       "      <th>rating_grade</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>Бинбанк</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Добрый день! Я не являюсь клиентом банка и пор...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>г. Новосибирск</td>\n",
       "      <td>Сбербанк России</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Доброго дня! Являюсь держателем зарплатной кар...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>Бинбанк</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Здравствуйте!  Дублирую свое заявление от 03.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>г. Ставрополь</td>\n",
       "      <td>Сбербанк России</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Добрый день!! Я открыл расчетный счет в СберБа...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>г. Челябинск</td>\n",
       "      <td>ОТП Банк</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04.03.2015 г. взяла кредит в вашем банке, заяв...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city        bank_name  rating_grade  \\\n",
       "0       г. Москва          Бинбанк           NaN   \n",
       "1  г. Новосибирск  Сбербанк России           NaN   \n",
       "2       г. Москва          Бинбанк           NaN   \n",
       "3   г. Ставрополь  Сбербанк России           NaN   \n",
       "4    г. Челябинск         ОТП Банк           NaN   \n",
       "\n",
       "                                                text  \n",
       "0  Добрый день! Я не являюсь клиентом банка и пор...  \n",
       "1  Доброго дня! Являюсь держателем зарплатной кар...  \n",
       "2  Здравствуйте!  Дублирую свое заявление от 03.0...  \n",
       "3  Добрый день!! Я открыл расчетный счет в СберБа...  \n",
       "4  04.03.2015 г. взяла кредит в вашем банке, заяв...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef54f582-68f9-4c1c-bf1c-12684571aaa6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 153499 entries, 0 to 153498\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   city          138325 non-null  object \n",
      " 1   bank_name     153499 non-null  object \n",
      " 2   rating_grade  88658 non-null   float64\n",
      " 3   text          153499 non-null  object \n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab0fe38-f6c8-4e4f-8c6f-104d2cc34465",
   "metadata": {},
   "source": [
    "##### 1.1. Посчитайте количество отзывов в разных городах и на разные банки "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63d75fe1-0ca6-4fc2-b6a5-ae0d7a62e139",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>bank_name</th>\n",
       "      <th>N_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>Сбербанк России</td>\n",
       "      <td>8146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>Альфа-Банк</td>\n",
       "      <td>3871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>Русский Стандарт</td>\n",
       "      <td>2801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>ВТБ 24</td>\n",
       "      <td>2714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>г. Санкт-Петербург</td>\n",
       "      <td>Сбербанк России</td>\n",
       "      <td>2434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18764</th>\n",
       "      <td>г. Москва,  Отделение \"Жулебинское\"</td>\n",
       "      <td>Московский Кредитный Банк</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18765</th>\n",
       "      <td>г. Москва, \"Китай-город\"</td>\n",
       "      <td>Фора-Банк</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18766</th>\n",
       "      <td>г. Москва, Ветошный пер</td>\n",
       "      <td>Восточный Экспресс Банк</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18767</th>\n",
       "      <td>г. Москва, Видное</td>\n",
       "      <td>Сбербанк России</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18768</th>\n",
       "      <td>г. … или другой населенный пункт</td>\n",
       "      <td>Лето Банк</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18769 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      city                  bank_name  \\\n",
       "0                                г. Москва            Сбербанк России   \n",
       "1                                г. Москва                 Альфа-Банк   \n",
       "2                                г. Москва           Русский Стандарт   \n",
       "3                                г. Москва                     ВТБ 24   \n",
       "4                       г. Санкт-Петербург            Сбербанк России   \n",
       "...                                    ...                        ...   \n",
       "18764  г. Москва,  Отделение \"Жулебинское\"  Московский Кредитный Банк   \n",
       "18765             г. Москва, \"Китай-город\"                  Фора-Банк   \n",
       "18766              г. Москва, Ветошный пер    Восточный Экспресс Банк   \n",
       "18767                    г. Москва, Видное            Сбербанк России   \n",
       "18768     г. … или другой населенный пункт                  Лето Банк   \n",
       "\n",
       "       N_comments  \n",
       "0            8146  \n",
       "1            3871  \n",
       "2            2801  \n",
       "3            2714  \n",
       "4            2434  \n",
       "...           ...  \n",
       "18764           1  \n",
       "18765           1  \n",
       "18766           1  \n",
       "18767           1  \n",
       "18768           1  \n",
       "\n",
       "[18769 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['city', 'bank_name'], as_index=False)\\\n",
    "    .count()\\\n",
    "    .sort_values('text', ascending=False)\\\n",
    "    .rename(columns = {'text':'N_comments'})\\\n",
    "    .reset_index()[['city', 'bank_name','N_comments']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f069df8-2e8b-4c73-bac3-bcdadaad5ced",
   "metadata": {},
   "source": [
    "##### 1.2. Постройте гистограмы длин ~~слов~~ **(текстов ???)** в символах и в словах  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "25fb9fd1-beee-4179-a9e9-844636c58f66",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max length = 32082 \n",
      "min length = 23\n"
     ]
    }
   ],
   "source": [
    "len_data = df.text.apply(len)\n",
    "length = len_data.tolist()\n",
    "\n",
    "print('max length =',max(length),'\\n''min length =',min(length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8772d4f6-31aa-4087-9ece-90c208228fa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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TTaaZK1phMbHebgMAgHqHNU0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABtqFZrat2+vEydOVNtfXFys9u3b/+qmAAAAfE2tQtPBgwdVUVFRbX9paakOHz78q5sCAADwNY2upfjTTz+1/r1u3ToFBwdbjysqKpSZmal27drVWXMAAAC+4ppC09ChQyVJfn5+Gj16tMdY48aN1a5dO7355pt11hwAAICvuKbQVFlZKUmKjo7WN998o7CwsOvSFAAAgK+5ptBU5cCBA3XdBwAAgE+rVWiSpMzMTGVmZurYsWPWO1BVFi1a9KsbAwAA8CW1Ck0zZszQzJkz1atXL7Vq1Up+fn513RcAAIBPqVVoWrhwodLT0/XEE0/UdT8AAAA+qVb3aSorK1OfPn3quhcAAACfVavQ9Mwzz2jp0qV13QsAAIDPqtXHc+fPn9d7772n9evXq3v37mrcuLHH+Jw5c+qkOQAAAF9Rq9C0a9cu9ezZU5KUl5fnMcaicAAA0BDVKjRt3LixrvsAAADwabVa01RXtmzZooceekiRkZHy8/PTypUrPcaffPJJ+fn5eWyDBg3yqDl58qRGjRqloKAghYSEaMyYMTpz5oxHza5du3TfffcpMDBQUVFRmj17drVeVqxYoU6dOikwMFCxsbFas2ZNnZ8vAACov2r1TlP//v2v+DHchg0bbB3n7Nmz6tGjh55++mk9+uijNdYMGjRIixcvth47nU6P8VGjRuno0aPKyMhQeXm5nnrqKY0bN85aqO52uzVw4EAlJiZq4cKFys3N1dNPP62QkBCNGzdOkrRt2zaNHDlSs2bN0oMPPqilS5dq6NCh+vbbb9WtWzdb5wIAABq2WoWmqvVMVcrLy5WTk6O8vLxqX+R7JYMHD9bgwYOvWON0OuVyuWoc+/7777V27Vp988036tWrlyRp/vz5euCBB/TGG28oMjJSS5YsUVlZmRYtWiSHw6GuXbsqJydHc+bMsULTvHnzNGjQIE2cOFGS9NprrykjI0MLFizQwoULbZ8PAABouGoVmt56660a90+fPr3aR2O/1qZNmxQeHq7mzZvr/vvv15///Ge1aNFCkpSVlaWQkBArMElSYmKi/P39tX37dj3yyCPKyspS37595XA4rJqkpCT99a9/1alTp9S8eXNlZWUpNTXV43mTkpKqfVx4sdLSUpWWllqP3W53HZ0xAADwRXW6punf/u3f6vR75wYNGqS//e1vyszM1F//+ldt3rxZgwcPVkVFhSSpsLBQ4eHhHj/TqFEjhYaGqrCw0KqJiIjwqKl6fLWaqvGazJo1S8HBwdYWFRX1604WAAD4tFp/YW9NsrKyFBgYWGfHGzFihPXv2NhYde/eXTExMdq0aZMGDBhQZ89TG5MnT/Z4d8rtdhOcAABowGoVmi5dtG2M0dGjR7Vjxw69+uqrddJYTdq3b6+wsDDt27dPAwYMkMvl0rFjxzxqLly4oJMnT1rroFwul4qKijxqqh5freZya6mkX9ZaXbooHQAANFy1+nju4o+lgoODFRoaqn79+mnNmjWaNm1aXfdo+emnn3TixAm1atVKkpSQkKDi4mJlZ2dbNRs2bFBlZaXi4+Otmi1btqi8vNyqycjIUMeOHdW8eXOrJjMz0+O5MjIylJCQcN3OBQAA1C+1eqfp4lsA/BpnzpzRvn37rMcHDhxQTk6OQkNDFRoaqhkzZmjYsGFyuVzav3+/XnrpJXXo0EFJSUmSpM6dO2vQoEEaO3asFi5cqPLycqWkpGjEiBGKjIyUJD3++OOaMWOGxowZo0mTJikvL0/z5s3zWMz+4osv6re//a3efPNNDRkyRMuWLdOOHTv03nvv1cl5AgCA+u9XrWnKzs7W999/L0nq2rWrfvOb31zTz+/YsUP9+/e3HletERo9erTeffdd7dq1Sx988IGKi4sVGRmpgQMH6rXXXvP4WGzJkiVKSUnRgAED5O/vr2HDhuntt9+2xoODg/X5558rOTlZcXFxCgsL09SpU63bDUhSnz59tHTpUk2ZMkWvvPKKbr/9dq1cuZJ7NAEAAEutQtOxY8c0YsQIbdq0SSEhIZKk4uJi9e/fX8uWLVPLli1tHadfv34yxlx2fN26dVc9RmhoqHUjy8vp3r27vvjiiyvWPPbYY3rssceu+nwAAODmVKs1TS+88IJOnz6t3bt36+TJkzp58qTy8vLkdrv1+9//vq57BAAA8LpavdO0du1arV+/Xp07d7b2denSRWlpaRo4cGCdNQcAAOAravVOU2VlpRo3blxtf+PGjVVZWfmrmwIAAPA1tQpN999/v1588UUdOXLE2nf48GFNmDDB6zedBAAAuB5qFZoWLFggt9utdu3aKSYmRjExMYqOjpbb7db8+fPrukcAAACvq9WapqioKH377bdav3699u7dK+mXeyYlJibWaXMAAAC+4preadqwYYO6dOkit9stPz8//dM//ZNeeOEFvfDCC7rrrrvUtWvXq17aDwAAUB9dU2iaO3euxo4dq6CgoGpjwcHB+t3vfqc5c+bUWXMAAAC+4ppC03fffadBgwZddnzgwIEe3wMHAADQUFxTaCoqKqrxVgNVGjVqpOPHj//qpgAAAHzNNYWm2267TXl5eZcd37Vrl1q1avWrmwIAAPA11xSaHnjgAb366qs6f/58tbFz585p2rRpevDBB+usOQAAAF9xTbccmDJlij766CPdcccdSklJUceOHSVJe/fuVVpamioqKvSnP/3pujQKAADgTdcUmiIiIrRt2zY999xzmjx5sowxkiQ/Pz8lJSUpLS1NERER16VRAAAAb7rmm1u2bdtWa9as0alTp7Rv3z4ZY3T77berefPm16M/XEeVF8qVm5tbbX9cXJycTqcXOgIAwHfV6o7gktS8eXPdddddddkLbjB3UYHmHzwn149+1r7iw/v19vNSnz59vNgZAAC+p9ahCQ1DM1e0wmJivd0GAAA+r1Zf2AsAAHCzITQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2ODV0LRlyxY99NBDioyMlJ+fn1auXOkxbozR1KlT1apVKzVp0kSJiYn64YcfPGpOnjypUaNGKSgoSCEhIRozZozOnDnjUbNr1y7dd999CgwMVFRUlGbPnl2tlxUrVqhTp04KDAxUbGys1qxZU+fnCwAA6i+vhqazZ8+qR48eSktLq3F89uzZevvtt7Vw4UJt375dt9xyi5KSknT+/HmrZtSoUdq9e7cyMjK0atUqbdmyRePGjbPG3W63Bg4cqLZt2yo7O1uvv/66pk+frvfee8+q2bZtm0aOHKkxY8Zo586dGjp0qIYOHaq8vLzrd/IAAKBeaeTNJx88eLAGDx5c45gxRnPnztWUKVP08MMPS5L+9re/KSIiQitXrtSIESP0/fffa+3atfrmm2/Uq1cvSdL8+fP1wAMP6I033lBkZKSWLFmisrIyLVq0SA6HQ127dlVOTo7mzJljhat58+Zp0KBBmjhxoiTptddeU0ZGhhYsWKCFCxfegJkAAAC+zmfXNB04cECFhYVKTEy09gUHBys+Pl5ZWVmSpKysLIWEhFiBSZISExPl7++v7du3WzV9+/aVw+GwapKSkpSfn69Tp05ZNRc/T1VN1fPUpLS0VG6322MDAAANl8+GpsLCQklSRESEx/6IiAhrrLCwUOHh4R7jjRo1UmhoqEdNTce4+DkuV1M1XpNZs2YpODjY2qKioq71FAEAQD3is6HJ102ePFklJSXWdujQIW+3BAAAriOfDU0ul0uSVFRU5LG/qKjIGnO5XDp27JjH+IULF3Ty5EmPmpqOcfFzXK6marwmTqdTQUFBHhsAAGi4fDY0RUdHy+VyKTMz09rndru1fft2JSQkSJISEhJUXFys7Oxsq2bDhg2qrKxUfHy8VbNlyxaVl5dbNRkZGerYsaOaN29u1Vz8PFU1Vc8DAADg1dB05swZ5eTkKCcnR9Ivi79zcnJUUFAgPz8/jR8/Xn/+85/16aefKjc3V//+7/+uyMhIDR06VJLUuXNnDRo0SGPHjtXXX3+tL7/8UikpKRoxYoQiIyMlSY8//rgcDofGjBmj3bt368MPP9S8efOUmppq9fHiiy9q7dq1evPNN7V3715Nnz5dO3bsUEpKyo2eEgAA4KO8esuBHTt2qH///tbjqiAzevRopaen66WXXtLZs2c1btw4FRcX695779XatWsVGBho/cySJUuUkpKiAQMGyN/fX8OGDdPbb79tjQcHB+vzzz9XcnKy4uLiFBYWpqlTp3rcy6lPnz5aunSppkyZoldeeUW33367Vq5cqW7dut2AWQAAAPWBV0NTv379ZIy57Lifn59mzpypmTNnXrYmNDRUS5cuveLzdO/eXV988cUVax577DE99thjV274JlB5oVy5ubke++Li4uR0Or3UEQAAvsGroQm+x11UoPkHz8n1o58kqfjwfr39/C/vxgEAcDMjNKGaZq5ohcXEersNAAB8is9ePQcAAOBLCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADY0MjbDcC3VV4oV25ubrX9cXFxcjqdXugIAADvIDThitxFBZp/8JxcP/pZ+4oP79fbz0t9+vTxYmcAANxYhCZcVTNXtMJiYr3dBgAAXsWaJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgQyNvN4CrKy0tVXZ2tvU4NzdXlZVebAgAgJsQoakeyM7O1u/f+UQht8VIkn7K+ULNO8R5uSsAAG4uhKZ6IuS2GIXFxEqSig/v93I3AADcfHx6TdP06dPl5+fnsXXq1MkaP3/+vJKTk9WiRQvdeuutGjZsmIqKijyOUVBQoCFDhqhp06YKDw/XxIkTdeHCBY+aTZs26c4775TT6VSHDh2Unp5+I04PAADUIz4dmiSpa9euOnr0qLVt3brVGpswYYL+8Y9/aMWKFdq8ebOOHDmiRx991BqvqKjQkCFDVFZWpm3btumDDz5Qenq6pk6datUcOHBAQ4YMUf/+/ZWTk6Px48frmWee0bp1627oeQIAAN/m8x/PNWrUSC6Xq9r+kpIS/ed//qeWLl2q+++/X5K0ePFide7cWV999ZXuvvtuff7559qzZ4/Wr1+viIgI9ezZU6+99pomTZqk6dOny+FwaOHChYqOjtabb74pSercubO2bt2qt956S0lJSTf0XAEAgO/y+XeafvjhB0VGRqp9+/YaNWqUCgoKJP2yOLq8vFyJiYlWbadOndSmTRtlZWVJkrKyshQbG6uIiAirJikpSW63W7t377ZqLj5GVU3VMQAAACQff6cpPj5e6enp6tixo44ePaoZM2bovvvuU15engoLC+VwOBQSEuLxMxERESosLJQkFRYWegSmqvGqsSvVuN1unTt3Tk2aNKmxt9LSUpWWllqP3W73rzrX+qTyQrlyc3M99sXFxcnpdHqpIwAArj+fDk2DBw+2/t29e3fFx8erbdu2Wr58+WXDzI0ya9YszZgxw6s9eIu7qEDzD56T60c/Sb9czff281KfPn283BkAANePz388d7GQkBDdcccd2rdvn1wul8rKylRcXOxRU1RUZK2Bcrlc1a6mq3p8tZqgoKArBrPJkyerpKTE2g4dOvRrT69eaeaKVlhMrMJiYq37RwEA0JDVq9B05swZ7d+/X61atVJcXJwaN26szMxMazw/P18FBQVKSEiQJCUkJCg3N1fHjh2zajIyMhQUFKQuXbpYNRcfo6qm6hiX43Q6FRQU5LEBAICGy6dD0x//+Edt3rxZBw8e1LZt2/TII48oICBAI0eOVHBwsMaMGaPU1FRt3LhR2dnZeuqpp5SQkKC7775bkjRw4EB16dJFTzzxhL777jutW7dOU6ZMUXJysrX+5tlnn9WPP/6ol156SXv37tU777yj5cuXa8KECd48dQAA4GN8ek3TTz/9pJEjR+rEiRNq2bKl7r33Xn311Vdq2bKlJOmtt96Sv7+/hg0bptLSUiUlJemdd96xfj4gIECrVq3Sc889p4SEBN1yyy0aPXq0Zs6cadVER0dr9erVmjBhgubNm6fWrVvr/fff53YDAADAg0+HpmXLll1xPDAwUGlpaUpLS7tsTdu2bbVmzZorHqdfv37auXNnrXoEAAA3B5/+eA4AAMBXEJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANvj0HcFRP1ReKFdubm61/XFxcdZ3/AEAUN8RmvCruYsKNP/gObl+9LP2FR/er7efl/r06ePFzgAAqDuEJtSJZq5ohcXEersNAACuG9Y0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGzg5pa4Lmr6ahW+VgUAUJ8RmnBdXPrVKnytCgCgviM04brhq1UAAA0Ja5oAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABq6eww1R032bJO7dBACoPwhNuCEuvW+TxL2bAAD1C6EJNwz3bQIA1GesaQIAALCB0AQAAGADoQkAAMAG1jTBa2q6oo6r6QAAvorQBK+59Io6rqYDAPgyQhO8iivqAAD1BWuaAAAAbCA0AQAA2EBoAgAAsIE1TfAZfD8dAMCXEZrgM/h+OgCALyM0wadcejUd93ICAPgKQhN8GvdyAgD4CkITfB73cgIA+AJCE+oVFosDALyF0IR6hcXiAABvITSh3mGxOADAGwhNqPcufffpZEG+ftcvV7GxnuugCFIAgF+D0IQG4eJ3n4oP79f8z3fzER4AoE4RmtAgXe0jvLKyMkmSw+Hw+DnejQIAXA6h6RJpaWl6/fXXVVhYqB49emj+/Pnq3bu3t9vCr3TpR3g/5WxRo1tD5erQzaqp6WM9QhQAoAqh6SIffvihUlNTtXDhQsXHx2vu3LlKSkpSfn6+wsPDvd0efqVLP8JrHOzyeDfq0o/1agpRl75DxTtWAHDzIDRdZM6cORo7dqyeeuopSdLChQu1evVqLVq0SC+//LKXu8ONcLW1UZe+Q2XnHauaghXhCwDqH0LT/ykrK1N2drYmT55s7fP391diYqKysrKq1ZeWlqq0tNR6XFJSIklyu9113tvZs2d14uAeXSg998tzHT2oRu4SORv7///nv2QfNXVUc0tza94lqeJCufzKy6x9lz6WpNPHftKfF+1RiOtbSdKJg3sUEHirQlxtrJpL99VU8/PJQr0wrL+6du0qAIAUHx9f58es+n/bGHP1YgNjjDGHDx82ksy2bds89k+cONH07t27Wv20adOMJDY2NjY2NrYGsB06dOiqWYF3mmpp8uTJSk1NtR5XVlbq5MmTatGihfz8/K7wk9fG7XYrKipKhw4dUlBQUJ0dtyFgbmrGvNSMebk85qZmzEvNGtq8GGN0+vRpRUZGXrWW0PR/wsLCFBAQoKKiIo/9RUVFcrlc1eqdTme1tSYhISHXrb+goKAG8eK8HpibmjEvNWNeLo+5qRnzUrOGNC/BwcG26vyvXnJzcDgciouLU2ZmprWvsrJSmZmZSkhI8GJnAADAF/BO00VSU1M1evRo9erVS71799bcuXN19uxZ62o6AABw8yI0XWT48OE6fvy4pk6dqsLCQvXs2VNr165VRESE13pyOp2aNm0al53XgLmpGfNSM+bl8pibmjEvNbuZ58XPGDvX2AEAANzcWNMEAABgA6EJAADABkITAACADYQmAAAAGwhNPi4tLU3t2rVTYGCg4uPj9fXXX3u7pTozffp0+fn5eWydOnWyxs+fP6/k5GS1aNFCt956q4YNG1bt5qMFBQUaMmSImjZtqvDwcE2cOFEXLlzwqNm0aZPuvPNOOZ1OdejQQenp6Tfi9K7Jli1b9NBDDykyMlJ+fn5auXKlx7gxRlOnTlWrVq3UpEkTJSYm6ocffvCoOXnypEaNGqWgoCCFhIRozJgxOnPmjEfNrl27dN999ykwMFBRUVGaPXt2tV5WrFihTp06KTAwULGxsVqzZk2dn69dV5uXJ598stpraNCgQR41DXFeZs2apbvuukvNmjVTeHi4hg4dqvz8fI+aG/n74yt/p+zMS79+/aq9Zp599lmPmoY2L5L07rvvqnv37tYNKRMSEvTZZ59Z4zfj66VW6uSL23BdLFu2zDgcDrNo0SKze/duM3bsWBMSEmKKioq83VqdmDZtmunatas5evSotR0/ftwaf/bZZ01UVJTJzMw0O3bsMHfffbfp06ePNX7hwgXTrVs3k5iYaHbu3GnWrFljwsLCzOTJk62aH3/80TRt2tSkpqaaPXv2mPnz55uAgACzdu3aG3quV7NmzRrzpz/9yXz00UdGkvn44489xv/yl7+Y4OBgs3LlSvPdd9+Zf/7nfzbR0dHm3LlzVs2gQYNMjx49zFdffWW++OIL06FDBzNy5EhrvKSkxERERJhRo0aZvLw88/e//900adLE/Md//IdV8+WXX5qAgAAze/Zss2fPHjNlyhTTuHFjk5ube93noCZXm5fRo0ebQYMGebyGTp486VHTEOclKSnJLF682OTl5ZmcnBzzwAMPmDZt2pgzZ85YNTfq98eX/k7ZmZff/va3ZuzYsR6vmZKSEmu8Ic6LMcZ8+umnZvXq1ea///u/TX5+vnnllVdM48aNTV5enjHm5ny91AahyYf17t3bJCcnW48rKipMZGSkmTVrlhe7qjvTpk0zPXr0qHGsuLjYNG7c2KxYscLa9/333xtJJisryxjzy3+o/v7+prCw0Kp59913TVBQkCktLTXGGPPSSy+Zrl27ehx7+PDhJikpqY7Ppu5cGg4qKyuNy+Uyr7/+urWvuLjYOJ1O8/e//90YY8yePXuMJPPNN99YNZ999pnx8/Mzhw8fNsYY884775jmzZtbc2OMMZMmTTIdO3a0Hv/rv/6rGTJkiEc/8fHx5ne/+12dnmNtXC40Pfzww5f9mZthXowx5tixY0aS2bx5szHmxv7++PLfqUvnxZhfQtOLL7542Z+5GealSvPmzc3777/P6+Ua8PGcjyorK1N2drYSExOtff7+/kpMTFRWVpYXO6tbP/zwgyIjI9W+fXuNGjVKBQUFkqTs7GyVl5d7nH+nTp3Upk0b6/yzsrIUGxvrcfPRpKQkud1u7d6926q5+BhVNfVpDg8cOKDCwkKP8wgODlZ8fLzHXISEhKhXr15WTWJiovz9/bV9+3arpm/fvnI4HFZNUlKS8vPzderUKaumvs3Xpk2bFB4ero4dO+q5557TiRMnrLGbZV5KSkokSaGhoZJu3O+Pr/+dunReqixZskRhYWHq1q2bJk+erJ9//tkauxnmpaKiQsuWLdPZs2eVkJDA6+UacEdwH/W///u/qqioqHY38oiICO3du9dLXdWt+Ph4paenq2PHjjp69KhmzJih++67T3l5eSosLJTD4aj2JcgREREqLCyUJBUWFtY4P1VjV6pxu906d+6cmjRpcp3Oru5UnUtN53HxeYaHh3uMN2rUSKGhoR410dHR1Y5RNda8efPLzlfVMXzNoEGD9Oijjyo6Olr79+/XK6+8osGDBysrK0sBAQE3xbxUVlZq/Pjxuueee9StWzdJumG/P6dOnfLZv1M1zYskPf7442rbtq0iIyO1a9cuTZo0Sfn5+froo48kNex5yc3NVUJCgs6fP69bb71VH3/8sbp06aKcnJyb/vViF6EJXjN48GDr3927d1d8fLzatm2r5cuX14swA+8bMWKE9e/Y2Fh1795dMTEx2rRpkwYMGODFzm6c5ORk5eXlaevWrd5uxadcbl7GjRtn/Ts2NlatWrXSgAEDtH//fsXExNzoNm+ojh07KicnRyUlJfqv//ovjR49Wps3b/Z2W/UKH8/5qLCwMAUEBFS7eqGoqEgul8tLXV1fISEhuuOOO7Rv3z65XC6VlZWpuLjYo+bi83e5XDXOT9XYlWqCgoLqTTCrOpcrvRZcLpeOHTvmMX7hwgWdPHmyTuarvrzm2rdvr7CwMO3bt09Sw5+XlJQUrVq1Shs3blTr1q2t/Tfq98dX/05dbl5qEh8fL0ker5mGOi8Oh0MdOnRQXFycZs2apR49emjevHk3/evlWhCafJTD4VBcXJwyMzOtfZWVlcrMzFRCQoIXO7t+zpw5o/3796tVq1aKi4tT48aNPc4/Pz9fBQUF1vknJCQoNzfX4z/FjIwMBQUFqUuXLlbNxceoqqlPcxgdHS2Xy+VxHm63W9u3b/eYi+LiYmVnZ1s1GzZsUGVlpfWfQkJCgrZs2aLy8nKrJiMjQx07dlTz5s2tmvo8Xz/99JNOnDihVq1aSWq482KMUUpKij7++GNt2LCh2seLN+r3x9f+Tl1tXmqSk5MjSR6vmYY2L5dTWVmp0tLSm/b1UiveXomOy1u2bJlxOp0mPT3d7Nmzx4wbN86EhIR4XL1Qn/3hD38wmzZtMgcOHDBffvmlSUxMNGFhYebYsWPGmF8ugW3Tpo3ZsGGD2bFjh0lISDAJCQnWz1ddAjtw4ECTk5Nj1q5da1q2bFnjJbATJ04033//vUlLS/PJWw6cPn3a7Ny50+zcudNIMnPmzDE7d+40//M//2OM+eWWAyEhIeaTTz4xu3btMg8//HCNtxz4zW9+Y7Zv3262bt1qbr/9do9L64uLi01ERIR54oknTF5enlm2bJlp2rRptUvrGzVqZN544w3z/fffm2nTpnn10vorzcvp06fNH//4R5OVlWUOHDhg1q9fb+68805z++23m/Pnz1vHaIjz8txzz5ng4GCzadMmj0vnf/75Z6vmRv3++NLfqavNy759+8zMmTPNjh07zIEDB8wnn3xi2rdvb/r27WsdoyHOizHGvPzyy2bz5s3mwIEDZteuXebll182fn5+5vPPPzfG3Jyvl9ogNPm4+fPnmzZt2hiHw2F69+5tvvrqK2+3VGeGDx9uWrVqZRwOh7ntttvM8OHDzb59+6zxc+fOmeeff940b97cNG3a1DzyyCPm6NGjHsc4ePCgGTx4sGnSpIkJCwszf/jDH0x5eblHzcaNG03Pnj2Nw+Ew7du3N4sXL74Rp3dNNm7caCRV20aPHm2M+eW2A6+++qqJiIgwTqfTDBgwwOTn53sc48SJE2bkyJHm1ltvNUFBQeapp54yp0+f9qj57rvvzL333mucTqe57bbbzF/+8pdqvSxfvtzccccdxuFwmK5du5rVq1dft/O+mivNy88//2wGDhxoWrZsaRo3bmzatm1rxo4dW+2Pb0Ocl5rmRJLHa/tG/v74yt+pq81LQUGB6du3rwkNDTVOp9N06NDBTJw40eM+TcY0vHkxxpinn37atG3b1jgcDtOyZUszYMAAKzAZc3O+XmrDzxhjbtz7WgAAAPUTa5oAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYMP/A67NKo+SVbBSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(length, bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed7af35e-8470-4d0b-940f-a0ce6ba43d76",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "regex = re.compile(\"[А-Яа-я]+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87f6ec0e-7d99-4177-a9f3-2b3e813a7cd8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def words_only(text, regex=regex):\n",
    "    try:\n",
    "        return \" \".join(regex.findall(text))\n",
    "    except:\n",
    "        return \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8eb00b45-b57c-4631-bb0c-34d649cef400",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.text = df.text.str.lower()\n",
    "df.text = df.text.apply(words_only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04c608ad-1a4c-44dc-8329-965d55d5e2c7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'добрый день я не являюсь клиентом банка и поручителем по кредитному договору а также не являюсь каким либо другим лицом письменно оформившим отношения с банком по поводу урегулирования чьей либо задолженности начиная с марта года начали приходить бесконечные письма из ооо примо коллект на мой адрес город москва уваровский переулок дом квартира с угрозами о возбуждении уголовного дела в отношении гражданина филиппова эдуарда владимировича который уклоняется от уплаты взятых им кредитов договор от года и договор от года со всей ответственностью хочу вас заверить что вышеуказанный гражданин которого вы разыскиваете мне не знаком и никогда в моем адресе не был зарегистрирован каким образом вы не удостоверившись в подлинности его документов оформили на его имя кредитный договор мне по меньшей мере не понятно и почему по вашей милости я должна переживать и бояться за себе и свое имущество письма доставляют мне серьезные неудобства нарушают мои гражданские права а также нарушают закон о защите персональных данных убедительно прошу прекратить посылать на мой адрес письма с угрозами по вопросам к которым я не имею отношения в случае отклонения моей претензии буду вынуждена обратиться с жалобами в другие органы росспотребнадзор центральный банк службу финансового омбудсмена кроме этого будет подано заявление в полицию и заявление в суд возмещение причиненного мне морального вреда надеюсь на скорое решение моего вопроса'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.text[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d131672-158f-4547-a310-8dea8337eb10",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.tokenize import WordPunctTokenizer\n",
    "wpt = WordPunctTokenizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "53da9984-c28f-4231-999d-f012ac53d72f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def token_apply(text):\n",
    "    return wpt.tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07e854ed-df0f-437e-93ed-825792d2282d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 14 s\n",
      "Wall time: 25.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['text_only_words'] = df.text.apply(token_apply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63360828-c1e7-4256-8e69-faa9fd119d0a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max length = 5001 \n",
      "min length = 0\n"
     ]
    }
   ],
   "source": [
    "len_data = df.text_only_words.apply(len)\n",
    "length = len_data.tolist()\n",
    "\n",
    "print('max length =',max(length),'\\n''min length =',min(length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "13bf19a8-d2ba-41e4-ad54-3e7ab7eb93ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8U++++65+/OMf6+jRo4qJiZEkrVy5Us8++6xOnDghh8OhZ599Vhs3blRJSYn1Xg8//LAqKiqUk5MjSUpKStIdd9yh5cuXS5Lq6uoUFxen6dOn67nnnrtq7z6fTxEREaqsrJTL5WrsqfGzfft2zXunRJ16DbT2fbFtvVpHuNV98J2XfS1JXx8o1sIHBmj48OFN2h8AAM3Ftfz9Dqp7miorKyVJHTp0kCQVFhaqpqZGycnJVk3fvn3VrVs3FRQUSJIKCgo0cOBAKzBJUkpKinw+n/bt22fVXHiM+pr6Y1RXV6uwsNCvJjQ0VMnJyVbNxaqqquTz+fw2AADQcgVNaKqrq9OMGTP0ox/9SAMGDJAkeb1eORwORUZG+tXGxMTI6/VaNRcGpvrx+rEr1fh8Pp09e1Zff/21amtrL1tTf4yLZWZmKiIiwtri4uIaduIAAKBZCJrQlJaWppKSEq1duzbQrdgyZ84cVVZWWtvhw4cD3RIAAGhCrQLdgCSlp6drw4YN2rZtm7p27Wrtd7vdqq6uVkVFhd/VpvLycrndbqvm4qfc6p+uu7Dm4ifuysvL5XK51KZNG4WFhSksLOyyNfXHuJjT6ZTT6WzYCQMAgGYnoFeajDFKT0/X22+/rfz8fMXHx/uNJyYmqnXr1srLy7P2lZaWqqysTB6PR5Lk8XhUXFzs95Rbbm6uXC6XEhISrJoLj1FfU38Mh8OhxMREv5q6ujrl5eVZNQAA4MYW0CtNaWlpWrNmjd555x21b9/eun8oIiJCbdq0UUREhKZMmaKMjAx16NBBLpdL06dPl8fj0Z13fvdU2OjRo5WQkKBHH31UixYtktfr1dy5c5WWlmZdCZo2bZqWL1+u2bNna/LkycrPz9dbb72ljRs3Wr1kZGRo0qRJGjp0qIYNG6YlS5bozJkzeuyxx67/xAAAgKAT0NC0YsUKSdKIESP89q9evVo///nPJUkvvfSSQkNDNX78eFVVVSklJUWvvPKKVRsWFqYNGzboySeflMfj0U033aRJkyZp4cKFVk18fLw2btyomTNnaunSperatatee+01paSkWDUTJkzQiRMnNG/ePHm9Xg0ZMkQ5OTmX3BwOAABuTAENTXaWiAoPD1dWVpaysrK+t6Z79+7atGnTFY8zYsQI7dmz54o16enpSk9Pv2pPAADgxhM0T88BAAAEM0ITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbWgW6AVw/dedrVFxc7LcvMTFRTqczQB0BANB8EJpuIL7yMi07dFbuL0MkSRVHDujlp6Thw4cHuDMAAIIfoekG094dr069Bga6DQAAmh3uaQIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADY0KDQ1LNnT33zzTeX7K+oqFDPnj1/cFMAAADBpkGh6dChQ6qtrb1kf1VVlY4cOfKDmwIAAAg21/Q1Kn/+85+tnzdv3qyIiAjrdW1trfLy8tSjR49Gaw4AACBYXFNoGjdunCQpJCREkyZN8htr3bq1evTooRdffLHRmgMAAAgW1xSa6urqJEnx8fH6+OOP1alTpyZpCgAAINhcU2iqd/DgwcbuAwAAIKg1KDRJUl5envLy8nT8+HHrClS9VatW/eDGAAAAgkmDQtNvfvMbLVy4UEOHDlWXLl0UEhLS2H0BAAAElQaFppUrVyo7O1uPPvpoY/cDAAAQlBq0TlN1dbWGDx/e2L0AAAAErQaFpscff1xr1qxp7F4AAACCVoM+njt37pxeffVVvf/++xo0aJBat27tN7548eJGaQ4AACBYNCg07d27V0OGDJEklZSU+I1xUzgAAGiJGhSaPvjgg8buAwAAIKg16J4mAACAG02DrjSNHDnyih/D5efnN7ghAACAYNSg0FR/P1O9mpoaFRUVqaSk5JIv8gUAAGgJGhSaXnrppcvuX7BggU6fPv2DGgIAAAhGjXpP0z//8z/zvXMAAKBFatTQVFBQoPDwcNv127Zt0/3336/Y2FiFhIRo/fr1fuM///nPFRIS4reNGTPGr+bkyZOaOHGiXC6XIiMjNWXKlEuudu3du1d33323wsPDFRcXp0WLFl3Sy7p169S3b1+Fh4dr4MCB2rRpk/0TBwAALV6DPp578MEH/V4bY3Ts2DHt3r1bv/71r20f58yZMxo8eLAmT558yTHrjRkzRqtXr7ZeO51Ov/GJEyfq2LFjys3NVU1NjR577DE98cQT1orlPp9Po0ePVnJyslauXKni4mJNnjxZkZGReuKJJyRJ27dv1yOPPKLMzEz9+Mc/1po1azRu3Dh98sknGjBggO3zAQAALVeDQlNERITf69DQUPXp00cLFy7U6NGjbR9n7NixGjt27BVrnE6n3G73Zcc+/fRT5eTk6OOPP9bQoUMlScuWLdN9992n3//+94qNjdUbb7yh6upqrVq1Sg6HQ/3791dRUZEWL15shaalS5dqzJgxmjVrliTp+eefV25urpYvX66VK1faPh8AANByNSg0XXjlp6lt2bJF0dHRioqK0r333qvf/va36tixo6TvPg6MjIy0ApMkJScnKzQ0VDt37tRPf/pTFRQU6J577pHD4bBqUlJS9O///u/69ttvFRUVpYKCAmVkZPi9b0pKyiUfF16oqqpKVVVV1mufz9dIZwwAAIJRg0JTvcLCQn366aeSpP79++u2225rlKbqjRkzRg8++KDi4+N14MAB/fKXv9TYsWNVUFCgsLAweb1eRUdH+/1Oq1at1KFDB3m9XkmS1+tVfHy8X01MTIw1FhUVJa/Xa+27sKb+GJeTmZmp3/zmN41xmgAAoBloUGg6fvy4Hn74YW3ZskWRkZGSpIqKCo0cOVJr165V586dG6W5hx9+2Pp54MCBGjRokHr16qUtW7Zo1KhRjfIeDTVnzhy/q1M+n09xcXEB7AgAADSlBj09N336dJ06dUr79u3TyZMndfLkSZWUlMjn8+kXv/hFY/do6dmzpzp16qQvvvhCkuR2u3X8+HG/mvPnz+vkyZPWfVBut1vl5eV+NfWvr1bzffdSSd/da+Vyufw2AADQcjUoNOXk5OiVV15Rv379rH0JCQnKysrSu+++22jNXeyrr77SN998oy5dukiSPB6PKioqVFhYaNXk5+errq5OSUlJVs22bdtUU1Nj1eTm5qpPnz6KioqyavLy8vzeKzc3Vx6Pp8nOBQAANC8NCk11dXVq3br1Jftbt26turo628c5ffq0ioqKVFRUJEk6ePCgioqKVFZWptOnT2vWrFnasWOHDh06pLy8PD3wwAPq3bu3UlJSJEn9+vXTmDFjNHXqVO3atUt//etflZ6erocfflixsbGSpJ/97GdyOByaMmWK9u3bpzfffFNLly71+2jt6aefVk5Ojl588UV99tlnWrBggXbv3q309PSGTA8AAGiBGhSa7r33Xj399NM6evSote/IkSOaOXPmNd1rtHv3bt12223WDeQZGRm67bbbNG/ePIWFhWnv3r36yU9+oltvvVVTpkxRYmKiPvzwQ7+1mt544w317dtXo0aN0n333ae77rpLr776qjUeERGh9957TwcPHlRiYqKeeeYZzZs3z1puQJKGDx+uNWvW6NVXX9XgwYP1xz/+UevXr2eNJgAAYGnQjeDLly/XT37yE/Xo0cO6+fnw4cMaMGCA/vu//9v2cUaMGCFjzPeOb968+arH6NChg7WQ5fcZNGiQPvzwwyvWPPTQQ3rooYeu+n4AAODG1KDQFBcXp08++UTvv/++PvvsM0nffVSWnJzcqM0BAAAEi2v6eC4/P18JCQny+XwKCQnRP/7jP2r69OmaPn267rjjDvXv3/+qV3QAAACao2sKTUuWLNHUqVMv+3h9RESE/vVf/1WLFy9utOYAAACCxTWFpr/97W8aM2bM946PHj3a7/F/AACAluKaQlN5eflllxqo16pVK504ceIHNwUAABBsrik03XzzzSopKfne8b1791oLTwIAALQk1xSa7rvvPv3617/WuXPnLhk7e/as5s+frx//+MeN1hwAAECwuKYlB+bOnas//elPuvXWW5Wenq4+ffpIkj777DNlZWWptrZWv/rVr5qkUQAAgEC6ptAUExOj7du368knn9ScOXOshSlDQkKUkpKirKwsxcTENEmjAAAAgXTNi1t2795dmzZt0rfffqsvvvhCxhjdcsst1pffAgAAtEQNWhFckqKionTHHXc0Zi8AAABBq0Ff2AsAAHCjITQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADQ1ecgDNX935GhUXF1+yPzExUU6nMwAdAQAQvAhNNzBfeZmWHTor95ch1r6KIwf08lPS8OHDA9gZAADBh9B0g2vvjlenXgMD3QYAAEGPe5oAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADAhoCGpm3btun+++9XbGysQkJCtH79er9xY4zmzZunLl26qE2bNkpOTtbnn3/uV3Py5ElNnDhRLpdLkZGRmjJlik6fPu1Xs3fvXt19990KDw9XXFycFi1adEkv69atU9++fRUeHq6BAwdq06ZNjX6+AACg+QpoaDpz5owGDx6srKysy44vWrRIL7/8slauXKmdO3fqpptuUkpKis6dO2fVTJw4Ufv27VNubq42bNigbdu26YknnrDGfT6fRo8ere7du6uwsFC/+93vtGDBAr366qtWzfbt2/XII49oypQp2rNnj8aNG6dx48appKSk6U4eAAA0K60C+eZjx47V2LFjLztmjNGSJUs0d+5cPfDAA5Kk//qv/1JMTIzWr1+vhx9+WJ9++qlycnL08ccfa+jQoZKkZcuW6b777tPvf/97xcbG6o033lB1dbVWrVolh8Oh/v37q6ioSIsXL7bC1dKlSzVmzBjNmjVLkvT8888rNzdXy5cv18qVK6/DTAAAgGAXtPc0HTx4UF6vV8nJyda+iIgIJSUlqaCgQJJUUFCgyMhIKzBJUnJyskJDQ7Vz506r5p577pHD4bBqUlJSVFpaqm+//daqufB96mvq3+dyqqqq5PP5/DYAANByBW1o8nq9kqSYmBi//TExMdaY1+tVdHS033irVq3UoUMHv5rLHePC9/i+mvrxy8nMzFRERIS1xcXFXespAgCAZiRoQ1OwmzNnjiorK63t8OHDgW4JAAA0oaANTW63W5JUXl7ut7+8vNwac7vdOn78uN/4+fPndfLkSb+ayx3jwvf4vpr68ctxOp1yuVx+GwAAaLmCNjTFx8fL7XYrLy/P2ufz+bRz5055PB5JksfjUUVFhQoLC62a/Px81dXVKSkpyarZtm2bampqrJrc3Fz16dNHUVFRVs2F71NfU/8+AAAAAQ1Np0+fVlFRkYqKiiR9d/N3UVGRysrKFBISohkzZui3v/2t/vznP6u4uFj/8i//otjYWI0bN06S1K9fP40ZM0ZTp07Vrl279Ne//lXp6el6+OGHFRsbK0n62c9+JofDoSlTpmjfvn168803tXTpUmVkZFh9PP3008rJydGLL76ozz77TAsWLNDu3buVnp5+vacEAAAEqYAuObB7926NHDnSel0fZCZNmqTs7GzNnj1bZ86c0RNPPKGKigrdddddysnJUXh4uPU7b7zxhtLT0zVq1CiFhoZq/Pjxevnll63xiIgIvffee0pLS1NiYqI6deqkefPm+a3lNHz4cK1Zs0Zz587VL3/5S91yyy1av369BgwYcB1mAQAANAcBDU0jRoyQMeZ7x0NCQrRw4UItXLjwe2s6dOigNWvWXPF9Bg0apA8//PCKNQ899JAeeuihKzcMAABuWEF7TxMAAEAwITQBAADYQGgCAACwgdAEAABgA6EJAADAhoA+PYfgU3e+RsXFxX77EhMT5XQ6A9QRAADBgdAEP77yMi07dFbuL0MkSRVHDujlp75bywoAgBsZoQmXaO+OV6deAwPdBgAAQYV7mgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGxoFegGENzqzteouLj4kv2JiYlyOp0B6AgAgMAgNOGKfOVlWnborNxfhlj7Ko4c0MtPScOHDw9gZwAAXF+EJlxVe3e8OvUaGOg2AAAIKO5pAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGxgnaZmoKqqSoWFhdbr4uJi1dUFsCEAAG5AhKZmoLCwUL945R1F3txLkvRV0YeK6p0Y4K4AALixEJqaicibe1mrclccORDgbgAAuPFwTxMAAIANhCYAAAAbCE0AAAA2BHVoWrBggUJCQvy2vn37WuPnzp1TWlqaOnbsqHbt2mn8+PEqLy/3O0ZZWZlSU1PVtm1bRUdHa9asWTp//rxfzZYtW3T77bfL6XSqd+/eys7Ovh6nBwAAmpGgDk2S1L9/fx07dszaPvroI2ts5syZ+stf/qJ169Zp69atOnr0qB588EFrvLa2Vqmpqaqurtb27dv1+uuvKzs7W/PmzbNqDh48qNTUVI0cOVJFRUWaMWOGHn/8cW3evPm6nicAAAhuQf/0XKtWreR2uy/ZX1lZqf/8z//UmjVrdO+990qSVq9erX79+mnHjh2688479d5772n//v16//33FRMToyFDhuj555/Xs88+qwULFsjhcGjlypWKj4/Xiy++KEnq16+fPvroI7300ktKSUm5rucKAACCV9Bfafr8888VGxurnj17auLEiSorK5P03dpFNTU1Sk5Otmr79u2rbt26qaCgQJJUUFCggQMHKiYmxqpJSUmRz+fTvn37rJoLj1FfU3+M71NVVSWfz+e3AQCAliuoQ1NSUpKys7OVk5OjFStW6ODBg7r77rt16tQpeb1eORwORUZG+v1OTEyMvF6vJMnr9foFpvrx+rEr1fh8Pp09e/Z7e8vMzFRERIS1xcXF/dDTBQAAQSyoP54bO3as9fOgQYOUlJSk7t2766233lKbNm0C2Jk0Z84cZWRkWK99Ph/BCQCAFiyorzRdLDIyUrfeequ++OILud1uVVdXq6Kiwq+mvLzcugfK7XZf8jRd/eur1bhcrisGM6fTKZfL5bcBAICWq1mFptOnT+vAgQPq0qWLEhMT1bp1a+Xl5VnjpaWlKisrk8fjkSR5PB4VFxfr+PHjVk1ubq5cLpcSEhKsmguPUV9Tfwxcqu58jYqLi7V9+3Zrq6qqCnRbAAA0qaD+eO7f/u3fdP/996t79+46evSo5s+fr7CwMD3yyCOKiIjQlClTlJGRoQ4dOsjlcmn69OnyeDy68847JUmjR49WQkKCHn30US1atEher1dz585VWlqanE6nJGnatGlavny5Zs+ercmTJys/P19vvfWWNm7cGMhTD2q+8jItO3RW7i9DJH33XXgvPyUNHz48wJ0BANB0gjo0ffXVV3rkkUf0zTffqHPnzrrrrru0Y8cOde7cWZL00ksvKTQ0VOPHj1dVVZVSUlL0yiuvWL8fFhamDRs26Mknn5TH49FNN92kSZMmaeHChVZNfHy8Nm7cqJkzZ2rp0qXq2rWrXnvtNZYbuIr27njrC4QBALgRBHVoWrt27RXHw8PDlZWVpaysrO+t6d69uzZt2nTF44wYMUJ79uxpUI8AAODG0KzuaQIAAAgUQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAG4L6u+fQPNSdr1FxcfEl+xMTE+V0OgPQEQAAjY/QhB/MV16mZYfOyv1liLWv4sgBvfyUNHz48AB2BgBA4yE0oVG0d8erU6+BgW4DAIAmwz1NAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAaWHECTuNyClyx2CQBozghNaBIXL3jJYpcAgOaO0IQmw4KXAICWhHuaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA0sOYDr4nKLXUoseAkAaD4ITbguLl7sUmLBSwBA80JownXDYpcAgOaMe5oAAABsIDQBAADYQGgCAACwgXuaEDCXe6KOp+kAAMGK0ISAufiJOp6mAwAEM0ITAoon6gAAzQX3NAEAANjAlSYEDVYNBwAEM0ITggarhgMAghmhCUGFe5wAAMGK0ISgxrIEAIBgQWhCUGNZAgBAsCA0Iehd+JEdN4sDAAKF0IRmhZvFAQCBQmhCs3PxzeLc9wQAuB4ITWj2Lr76dLKsVP86olgDB/o/hUeQAgD8EIQmtAgXXn2qOHJAy97b5/cR3uWCFCEKAHAtCE0XycrK0u9+9zt5vV4NHjxYy5Yt07BhwwLdFq7RxR/hXRykLheiqqurJUkOh8PaR7ACANQjNF3gzTffVEZGhlauXKmkpCQtWbJEKSkpKi0tVXR0dKDbww90tatRXxVtU6t2HeTuPUASH/MBAPwRmi6wePFiTZ06VY899pgkaeXKldq4caNWrVql5557LsDdobFd7mpU6wj3NX3Md7mrUxfva2iNREADgGBCaPr/qqurVVhYqDlz5lj7QkNDlZycrIKCgkvqq6qqVFVVZb2urKyUJPl8vkbv7cyZM/rm0H6drzr73XsdO6RWvko5W4f+3/tftI+aRqq5Kcqad0k6dfwr/XbVfkW6P5EkfXNov8LC2ynS3c2quXhfQ2v+ftKr6eNHqn///gIASElJSY1+zPq/28aYqxcbGGOMOXLkiJFktm/f7rd/1qxZZtiwYZfUz58/30hiY2NjY2NjawHb4cOHr5oVuNLUQHPmzFFGRob1uq6uTidPnlTHjh0VEhJyhd+8dj6fT3FxcTp8+LBcLlejHhv/h3m+Ppjn64N5vj6Y5+unqebaGKNTp04pNjb2qrWEpv+vU6dOCgsLU3l5ud/+8vJyud3uS+qdTucl95pERkY2ZYtyuVz8o7wOmOfrg3m+Ppjn64N5vn6aYq4jIiJs1YVeveTG4HA4lJiYqLy8PGtfXV2d8vLy5PF4AtgZAAAIBlxpukBGRoYmTZqkoUOHatiwYVqyZInOnDljPU0HAABuXISmC0yYMEEnTpzQvHnz5PV6NWTIEOXk5CgmJiagfTmdTs2fP59Hz5sY83x9MM/XB/N8fTDP108wzHWIMXaesQMAALixcU8TAACADYQmAAAAGwhNAAAANhCaAAAAbCA0BbmsrCz16NFD4eHhSkpK0q5duwLdUlDbtm2b7r//fsXGxiokJETr16/3GzfGaN68eerSpYvatGmj5ORkff755341J0+e1MSJE+VyuRQZGakpU6bo9OnTfjV79+7V3XffrfDwcMXFxWnRokVNfWpBJTMzU3fccYfat2+v6OhojRs3TqWlpX41586dU1pamjp27Kh27dpp/PjxlyweW1ZWptTUVLVt21bR0dGaNWuWzp8/71ezZcsW3X777XI6nerdu7eys7Ob+vSCxooVKzRo0CBrMT+Px6N3333XGmeOm8YLL7ygkJAQzZgxw9rHXP9wCxYsUEhIiN/Wt29fa7xZzHGjfHEbmsTatWuNw+Ewq1atMvv27TNTp041kZGRpry8PNCtBa1NmzaZX/3qV+ZPf/qTkWTefvttv/EXXnjBREREmPXr15u//e1v5ic/+YmJj483Z8+etWrGjBljBg8ebHbs2GE+/PBD07t3b/PII49Y45WVlSYmJsZMnDjRlJSUmD/84Q+mTZs25j/+4z+u12kGXEpKilm9erUpKSkxRUVF5r777jPdunUzp0+ftmqmTZtm4uLiTF5entm9e7e58847zfDhw63x8+fPmwEDBpjk5GSzZ88es2nTJtOpUyczZ84cq+bLL780bdu2NRkZGWb//v1m2bJlJiwszOTk5FzX8w2UP//5z2bjxo3mf/7nf0xpaan55S9/aVq3bm1KSkqMMcxxU9i1a5fp0aOHGTRokHn66aet/cz1Dzd//nzTv39/c+zYMWs7ceKENd4c5pjQFMSGDRtm0tLSrNe1tbUmNjbWZGZmBrCr5uPi0FRXV2fcbrf53e9+Z+2rqKgwTqfT/OEPfzDGGLN//34jyXz88cdWzbvvvmtCQkLMkSNHjDHGvPLKKyYqKspUVVVZNc8++6zp06dPE59R8Dp+/LiRZLZu3WqM+W5eW7dubdatW2fVfPrpp0aSKSgoMMZ8F3BDQ0ON1+u1alasWGFcLpc1t7Nnzzb9+/f3e68JEyaYlJSUpj6loBUVFWVee+015rgJnDp1ytxyyy0mNzfX/MM//IMVmpjrxjF//nwzePDgy441lznm47kgVV1drcLCQiUnJ1v7QkNDlZycrIKCggB21nwdPHhQXq/Xb04jIiKUlJRkzWlBQYEiIyM1dOhQqyY5OVmhoaHauXOnVXPPPffI4XBYNSkpKSotLdW33357nc4muFRWVkqSOnToIEkqLCxUTU2N31z37dtX3bp185vrgQMH+i0em5KSIp/Pp3379lk1Fx6jvuZG/DdQW1urtWvX6syZM/J4PMxxE0hLS1Nqauol88FcN57PP/9csbGx6tmzpyZOnKiysjJJzWeOCU1B6uuvv1Ztbe0lq5HHxMTI6/UGqKvmrX7erjSnXq9X0dHRfuOtWrVShw4d/Goud4wL3+NGUldXpxkzZuhHP/qRBgwYIOm7eXA4HJd8ifXFc321efy+Gp/Pp7NnzzbF6QSd4uJitWvXTk6nU9OmTdPbb7+thIQE5riRrV27Vp988okyMzMvGWOuG0dSUpKys7OVk5OjFStW6ODBg7r77rt16tSpZjPHfI0KgB8kLS1NJSUl+uijjwLdSovUp08fFRUVqbKyUn/84x81adIkbd26NdBttSiHDx/W008/rdzcXIWHhwe6nRZr7Nix1s+DBg1SUlKSunfvrrfeektt2rQJYGf2caUpSHXq1ElhYWGXPDlQXl4ut9sdoK6at/p5u9Kcut1uHT9+3G/8/PnzOnnypF/N5Y5x4XvcKNLT07VhwwZ98MEH6tq1q7Xf7XarurpaFRUVfvUXz/XV5vH7alwuV7P5n+wP5XA41Lt3byUmJiozM1ODBw/W0qVLmeNGVFhYqOPHj+v2229Xq1at1KpVK23dulUvv/yyWrVqpZiYGOa6CURGRurWW2/VF1980Wz+eyY0BSmHw6HExETl5eVZ++rq6pSXlyePxxPAzpqv+Ph4ud1uvzn1+XzauXOnNacej0cVFRUqLCy0avLz81VXV6ekpCSrZtu2baqpqbFqcnNz1adPH0VFRV2nswksY4zS09P19ttvKz8/X/Hx8X7jiYmJat26td9cl5aWqqyszG+ui4uL/UJqbm6uXC6XEhISrJoLj1FfcyP/G6irq1NVVRVz3IhGjRql4uJiFRUVWdvQoUM1ceJE62fmuvGdPn1aBw4cUJcuXZrPf8+Ncjs5msTatWuN0+k02dnZZv/+/eaJJ54wkZGRfk8OwN+pU6fMnj17zJ49e4wks3jxYrNnzx7zv//7v8aY75YciIyMNO+8847Zu3eveeCBBy675MBtt91mdu7caT766CNzyy23+C05UFFRYWJiYsyjjz5qSkpKzNq1a03btm1vqCUHnnzySRMREWG2bNni9/jw3//+d6tm2rRpplu3biY/P9/s3r3beDwe4/F4rPH6x4dHjx5tioqKTE5OjuncufNlHx+eNWuW+fTTT01WVtYN9Yj2c889Z7Zu3WoOHjxo9u7da5577jkTEhJi3nvvPWMMc9yULnx6zhjmujE888wzZsuWLebgwYPmr3/9q0lOTjadOnUyx48fN8Y0jzkmNAW5ZcuWmW7duhmHw2GGDRtmduzYEeiWgtoHH3xgJF2yTZo0yRjz3bIDv/71r01MTIxxOp1m1KhRprS01O8Y33zzjXnkkUdMu3btjMvlMo899pg5deqUX83f/vY3c9dddxmn02luvvlm88ILL1yvUwwKl5tjSWb16tVWzdmzZ81TTz1loqKiTNu2bc1Pf/pTc+zYMb/jHDp0yIwdO9a0adPGdOrUyTzzzDOmpqbGr+aDDz4wQ4YMMQ6Hw/Ts2dPvPVq6yZMnm+7duxuHw2E6d+5sRo0aZQUmY5jjpnRxaGKuf7gJEyaYLl26GIfDYW6++WYzYcIE88UXX1jjzWGOQ4wxpnGuWQEAALRc3NMEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABv+H/1trAKwJQhRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(length, bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe3630a6-9598-4b47-98fe-f56a9ea33ce4",
   "metadata": {},
   "source": [
    "##### 1.3. Найдите 10 самых частых: слов, слов без стоп-слов, лемм, существительных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6eb2f845-f640-4ac0-b472-f806ab5cc8c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 153499/153499 [00:18<00:00, 8459.77it/s] \n"
     ]
    }
   ],
   "source": [
    "all_words =[]\n",
    "for i in tqdm(df.text):\n",
    "    all_words+=token_apply(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fd94c8b5-994f-47ee-96fa-ae35512cc27e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "93a88e10-f7e6-4424-9928-4f5377297781",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "word_freq = Counter(all_words)\n",
    "top_10 = word_freq.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b3590ab2-2373-4199-99d4-f00215b7a3d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "в:1330550\n",
      "и:1058997\n",
      "не:855335\n",
      "на:707935\n",
      "что:640057\n",
      "я:577500\n",
      "с:464037\n",
      "по:372769\n",
      "мне:320958\n",
      "банка:275906\n"
     ]
    }
   ],
   "source": [
    "for word, freq in top_10:\n",
    "    print(f'{word}:{freq}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c64a3988-f70c-4427-96e3-b791ca967795",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "import nltk\n",
    "#nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5540bc8-ec42-4950-bffb-2849693f33b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['и', 'в', 'во', 'не', 'что']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords.words('russian')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "72dcc6d2-a049-49d3-9df7-60f5dd412020",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mystopwords = stopwords.words('russian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "29e7910e-2800-43bf-94a3-575afc3db992",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 35429373/35429373 [00:49<00:00, 711075.56it/s]\n"
     ]
    }
   ],
   "source": [
    "all_words_new = [word for word in tqdm(all_words) if word not in mystopwords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "145ab19d-2cc9-4b49-864f-556294be3ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['добрый', 'день', 'являюсь', 'клиентом', 'банка']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_words_new[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2709db1a-c7e6-4d4f-b90a-9ef546405862",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "word_freq_no_stopwords = Counter(all_words_new)\n",
    "top_10_no_stopwords = word_freq_no_stopwords.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "11f1042e-a70d-41da-acb0-7b09ee0dcd26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "банка:275906\n",
      "банк:215426\n",
      "это:183365\n",
      "деньги:131201\n",
      "карту:105829\n",
      "карты:101821\n",
      "т:91756\n",
      "кредит:81136\n",
      "день:76552\n",
      "банке:72169\n"
     ]
    }
   ],
   "source": [
    "for word, freq in top_10_no_stopwords:\n",
    "    print(f'{word}:{freq}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7710f107-6560-4db8-bf7f-be6f303793f9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#pymorphy2 doesn't work correctly with Python 3.11+\n",
    "from pymorphy3 import MorphAnalyzer\n",
    "m = MorphAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4e5e4ef-b2a5-4b37-9de6-d328eca5528d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def lemmatize(text, mystem=m):\n",
    "    try:\n",
    "        return \" \".join([m.parse(w)[0].normal_form for w in text.split(' ')]).strip()  \n",
    "    except:\n",
    "        return \" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "06393af6-9b1d-4ad5-b08c-5b7d80622c6a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22351585/22351585 [52:31<00:00, 7092.54it/s] \n"
     ]
    }
   ],
   "source": [
    "all_words_lemmas = [lemmatize(word) for word in tqdm(all_words_new)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e7a6a5f3-0e39-427b-a0cc-65cae520b11d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "банк:668563\n",
      "карта:334999\n",
      "это:200057\n",
      "деньга:179819\n",
      "день:173116\n",
      "мой:163042\n",
      "кредит:156177\n",
      "который:149017\n",
      "отделение:144050\n",
      "клиент:136892\n",
      "CPU times: total: 4.34 s\n",
      "Wall time: 6.42 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "word_freq_lemmas = Counter(all_words_lemmas)\n",
    "top_10_lemmas = word_freq_lemmas.most_common(10)\n",
    "\n",
    "for word, freq in top_10_lemmas:\n",
    "    print(f'{word}:{freq}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b18f7d4-2d21-423d-a11f-64406558cb45",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22351585/22351585 [1:16:20<00:00, 4880.00it/s]  \n"
     ]
    }
   ],
   "source": [
    "all_words_parts = []\n",
    "for word in tqdm(all_words_lemmas):\n",
    "    parsed_word = m.parse(word)[0]\n",
    "    all_words_parts.append(parsed_word.tag.POS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "760343d3-f43d-433e-a9da-29187b3949d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOUN:10823937\n",
      "INFN:5572874\n",
      "ADJF:3331253\n",
      "ADVB:1253492\n",
      "PRCL:388209\n",
      "CONJ:308251\n",
      "None:189091\n",
      "NPRO:148808\n",
      "PREP:110536\n",
      "NUMR:66861\n",
      "CPU times: total: 28.5 s\n",
      "Wall time: 44.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "word_freq_parts = Counter(all_words_parts)\n",
    "top_10_parts = word_freq_parts.most_common(10)\n",
    "\n",
    "for word, freq in top_10_parts:\n",
    "    print(f'{word}:{freq}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d950ceeb-1cd0-4353-ae36-b236cb64daf7",
   "metadata": {},
   "source": [
    "##### 1.4. Постройте кривые Ципфа и Хипса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be48fca1-5e97-4743-82bf-c3d8e2e880b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "freqs = list(word_freq_lemmas.values())\n",
    "freqs = sorted(freqs, reverse = True)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(freqs[:300], range(300))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "0154a4c9-b928-4409-ac4c-66c26d748f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "#nltk.download('punkt') # закомментировал лишнюю команду\n",
    "from string import punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "899c38fe-847f-4783-a9a6-ad654ee19899",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 153499/153499 [05:50<00:00, 438.10it/s]\n"
     ]
    }
   ],
   "source": [
    "cnt = Counter()\n",
    "n_words = []\n",
    "n_tokens = []\n",
    "tokens = []\n",
    "for index, row in tqdm(df.iterrows(), total = len(df)):\n",
    "    tokens = word_tokenize(row['text'])\n",
    "    cnt.update([token for token in tokens if token not in punctuation])\n",
    "    n_words.append(len(cnt))\n",
    "    n_tokens.append(sum(cnt.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "717149f7-6ad1-433c-9048-8d040221acef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(n_tokens, n_words)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9598074d-ebf5-406b-bbad-1537fa1ae3b8",
   "metadata": {},
   "source": [
    "1.5. Ответьте на вопросы: какое слово встречается чаще, \"сотрудник\" или \"клиент\", сколько раз встречается слова \"мошенничество\" и \"доверие\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2923e7eb-212b-4377-96c4-716ebd368f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Слово \"клиент\" встречает чаще слова \"сотрудник\"\n",
    "word_freq_lemmas.get('сотрудник',0)>word_freq_lemmas.get('клиент',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "796b7b0e-e6fc-43fe-a73a-3d3a3a5ab29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_lemmas.get('мошеничество',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9a428551-10ca-41e4-b788-7008195e7143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2108"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_lemmas.get('доверие',0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea69d2f2-da73-4e8d-96c5-f6d00927978a",
   "metadata": {},
   "source": [
    "1.6. В поле \"rating_grade\" записана оценка отзыва по шкале от 1 до 5. Используйте меру Tf-idf\n",
    ", для того, чтобы найти ключевые слова и биграмы для положительных отзывов (с оценкой 5) и отрицательных отзывов (с оценкой 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f82299ca-75e0-4874-9d9c-65df9ddb0168",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a30a4602-8ad2-485d-91b8-3e048f5d1774",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(text, mystopwords = mystopwords):\n",
    "    try:\n",
    "        return \" \".join([token for token in text.split() if not token in mystopwords])\n",
    "    except:\n",
    "        return \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3f1e942b-265c-4d62-be55-aa9621eff47f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatize(text, mystem=m):\n",
    "    try:\n",
    "        return \" \".join([m.parse(w)[0].normal_form for w in text.split(' ')]).strip()  \n",
    "    except:\n",
    "        return \" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f5eaff1d-674c-4b18-a0e4-7c699ca905ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 48min 43s\n",
      "Wall time: 49min 48s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df.text = df.text.apply(remove_stopwords)\n",
    "df.text = df.text.apply(lemmatize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2432f810-d007-4898-9568-83cc6e399b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_positive = df[df['rating_grade'] == 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7f7cb164-df4f-4dbe-800e-9407c5bf5126",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_negative = df[df['rating_grade'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9323dda9-4751-4a5b-a4f1-10ad1be7151c",
   "metadata": {},
   "source": [
    "#### Первый способ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8521ce2e-1ac9-4afc-8be8-8155c77e5d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14713/14713 [00:00<00:00, 1659873.98it/s]\n"
     ]
    }
   ],
   "source": [
    "corpus_positive = [tex_pos for tex_pos in tqdm(df_positive.text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c1606ac-5857-467f-8cee-25bb8aa28a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_pos = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6193be88-f072-4f8b-b3b6-7eff1eefce5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_pos.fit(corpus_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "148517fb-1b68-4e67-9eb4-b9c01c33ee12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ключевые слова для текстов с позитивным настроем: [['банк' 'карта' 'вклад' ... 'парашют' 'сгнить' 'благоустроенный']]\n"
     ]
    }
   ],
   "source": [
    "# Считаем средние значения TF-IDF для каждого слова по всей коллекции позитивных текстов\n",
    "mean_tfidf_pos = tfidf_pos.transform(corpus_positive).mean(axis=0)\n",
    "\n",
    "# Получаем индексы слов с наибольшими значениями TF-IDF\n",
    "top_indices_pos = mean_tfidf_pos.argsort()[0, ::-1][:10]\n",
    "\n",
    "# Получаем список ключевых слов\n",
    "keywords_pos = tfidf_pos.get_feature_names_out()[top_indices_pos]\n",
    "\n",
    "print(\"Ключевые слова для текстов с позитивным настроем:\", keywords_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5ce63893-038c-42d6-847d-5243da952580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>карта</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>вклад</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>кредит</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>очень</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>отделение</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>это</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>день</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>клиент</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>сотрудник</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0\n",
       "0       банк\n",
       "1      карта\n",
       "2      вклад\n",
       "3     кредит\n",
       "4      очень\n",
       "5  отделение\n",
       "6        это\n",
       "7       день\n",
       "8     клиент\n",
       "9  сотрудник"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(keywords_pos).T[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d9f43e-98b1-4f43-b140-0de04fe8b378",
   "metadata": {},
   "source": [
    "#### Второй способ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0a17359a-7664-4a14-912b-402c54d26e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfIdfVectorizer_positive = TfidfVectorizer(use_idf = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0fe91af7-fffb-4f93-9383-dd2df2b7e2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfIdf_positive = tfIdfVectorizer_positive.fit_transform(df_positive.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7ed549c1-d6db-4990-b6c6-e69203f73cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>банк</td>\n",
       "      <td>1.088334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35432</th>\n",
       "      <td>это</td>\n",
       "      <td>1.598913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19943</th>\n",
       "      <td>очень</td>\n",
       "      <td>1.60139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6251</th>\n",
       "      <td>день</td>\n",
       "      <td>1.69696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11056</th>\n",
       "      <td>клиент</td>\n",
       "      <td>1.711944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10670</th>\n",
       "      <td>карта</td>\n",
       "      <td>1.769527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11957</th>\n",
       "      <td>который</td>\n",
       "      <td>1.807352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29509</th>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1.822706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5354</th>\n",
       "      <td>год</td>\n",
       "      <td>1.829849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19332</th>\n",
       "      <td>отделение</td>\n",
       "      <td>1.837514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1\n",
       "1370        банк  1.088334\n",
       "35432        это  1.598913\n",
       "19943      очень   1.60139\n",
       "6251        день   1.69696\n",
       "11056     клиент  1.711944\n",
       "10670      карта  1.769527\n",
       "11957    который  1.807352\n",
       "29509  сотрудник  1.822706\n",
       "5354         год  1.829849\n",
       "19332  отделение  1.837514"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([tfIdfVectorizer_positive.get_feature_names_out(),tfIdfVectorizer_positive.idf_]).\\\n",
    "            T.sort_values(1).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59c5f116-99b3-4de0-858f-20eecdaadd8a",
   "metadata": {},
   "source": [
    "#### Первый способ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8bcb2089-2e22-430e-8600-06e337a50246",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47387/47387 [00:00<00:00, 1923874.59it/s]\n"
     ]
    }
   ],
   "source": [
    "corpus_negative = [tex_neg for tex_neg in tqdm(df_negative.text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e13b97c6-9239-4b71-8f80-515e88831a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_neg = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dbce7309-d693-43b4-a7db-b3988e032537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_neg.fit(corpus_negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2868208d-d187-47f6-b91c-5e4060330a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ключевые слова для текстов с негативным настроем: [['банк' 'карта' 'деньга' ... 'правопреемство' 'никнуть' 'взыскиваеться']]\n"
     ]
    }
   ],
   "source": [
    "# Считаем средние значения TF-IDF для каждого слова по всей коллекции негативных текстов\n",
    "mean_tfidf_neg = tfidf_neg.transform(corpus_negative).mean(axis=0)\n",
    "\n",
    "# Получаем индексы слов с наибольшими значениями TF-IDF\n",
    "top_indices_neg = mean_tfidf_neg.argsort()[0, ::-1][:10]\n",
    "\n",
    "# Получаем список ключевых слов\n",
    "keywords_neg = tfidf_neg.get_feature_names_out()[top_indices_neg]\n",
    "\n",
    "print(\"Ключевые слова для текстов с негативным настроем:\", keywords_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0e43383e-6274-4a3f-aa02-4c7d6328cc81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>карта</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>деньга</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>кредит</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>отделение</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>день</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>это</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>счёт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>мой</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>заявление</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0\n",
       "0       банк\n",
       "1      карта\n",
       "2     деньга\n",
       "3     кредит\n",
       "4  отделение\n",
       "5       день\n",
       "6        это\n",
       "7       счёт\n",
       "8        мой\n",
       "9  заявление"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(keywords_neg).T[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa00be92-8ec5-4a9f-a72d-8b2099629bd6",
   "metadata": {},
   "source": [
    "#### Второй способ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2cc1e73f-1728-49ae-9dbe-e6a1de5fe3f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfIdfVectorizer_negative = TfidfVectorizer(use_idf = True)\n",
    "tfIdf_negative = tfIdfVectorizer_negative.fit_transform(df_negative.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e95b255a-e287-458d-ae68-3c8df3317ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2177</th>\n",
       "      <td>банк</td>\n",
       "      <td>1.101761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56109</th>\n",
       "      <td>это</td>\n",
       "      <td>1.448047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>день</td>\n",
       "      <td>1.522635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18998</th>\n",
       "      <td>который</td>\n",
       "      <td>1.612851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22945</th>\n",
       "      <td>мой</td>\n",
       "      <td>1.629741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>деньга</td>\n",
       "      <td>1.656029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17047</th>\n",
       "      <td>карта</td>\n",
       "      <td>1.71564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45272</th>\n",
       "      <td>сказать</td>\n",
       "      <td>1.723092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30790</th>\n",
       "      <td>отделение</td>\n",
       "      <td>1.732793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17638</th>\n",
       "      <td>клиент</td>\n",
       "      <td>1.749621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1\n",
       "2177        банк  1.101761\n",
       "56109        это  1.448047\n",
       "9981        день  1.522635\n",
       "18998    который  1.612851\n",
       "22945        мой  1.629741\n",
       "9982      деньга  1.656029\n",
       "17047      карта   1.71564\n",
       "45272    сказать  1.723092\n",
       "30790  отделение  1.732793\n",
       "17638     клиент  1.749621"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([tfIdfVectorizer_negative.get_feature_names_out(),tfIdfVectorizer_negative.idf_]).\\\n",
    "            T.sort_values(1).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17eb26d0-1b04-4613-9309-bfefa24a04ba",
   "metadata": {},
   "source": [
    "### А теперь для биграмм первым способом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f4734975-02dc-43da-a315-8b29ec2aace8",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_pos = TfidfVectorizer(ngram_range=(2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "64bd61d8-6d9f-4b93-b0db-c5c345b513d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(ngram_range=(2, 2))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(ngram_range=(2, 2))</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer(ngram_range=(2, 2))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_pos.fit(corpus_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8417ff1d-a035-48ae-a2c8-0b68d48a1ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ключевые биграммы для текстов с positive настроем: [array([['интернет банк', 'кредитный карта', 'сотрудник банк', ...,\n",
      "        'такой плавать', 'жильё иметь', 'действовать результат']],\n",
      "      dtype=object)]\n"
     ]
    }
   ],
   "source": [
    "mean_tfidf_pos = tfidf_pos.transform(corpus_positive).mean(axis=0)\n",
    "\n",
    "top_indices_pos = mean_tfidf_pos.argsort()[0, ::-1][:10]\n",
    "\n",
    "feature_names_pos = tfidf_pos.get_feature_names_out()\n",
    "bigram_keywords_pos = [feature_names_pos[idx] for idx in top_indices_pos]\n",
    "\n",
    "print(\"Ключевые биграммы для текстов с positive настроем:\", bigram_keywords_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e8e29407-335d-4683-9910-52aa3fdf39dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>интернет банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>кредитный карта</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>сотрудник банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>другой банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>клиент банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>отделение банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>являться клиент</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>следующий день</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>альфа банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>открыть вклад</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "0    интернет банк\n",
       "1  кредитный карта\n",
       "2   сотрудник банк\n",
       "3      другой банк\n",
       "4      клиент банк\n",
       "5   отделение банк\n",
       "6  являться клиент\n",
       "7   следующий день\n",
       "8       альфа банк\n",
       "9    открыть вклад"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(bigram_keywords_pos[0]).T[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "84b10b6e-af4d-4974-8aed-b5f91c0f4053",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_neg = TfidfVectorizer(ngram_range=(2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5acc8811-9b33-4383-8eef-36e335031e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(ngram_range=(2, 2))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(ngram_range=(2, 2))</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer(ngram_range=(2, 2))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_neg.fit(corpus_negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ca5650a5-920f-40e4-b0c7-db3d248aa11b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ключевые биграммы для текстов с негативным настроем: [array([['сотрудник банк', 'кредитный карта', 'денежный средство', ...,\n",
      "        'исполнять подписать', 'недопущение опись', 'ребёнок удивиться']],\n",
      "      dtype=object)]\n"
     ]
    }
   ],
   "source": [
    "mean_tfidf_neg = tfidf_neg.transform(corpus_negative).mean(axis=0)\n",
    "\n",
    "top_indices_neg = mean_tfidf_neg.argsort()[0, ::-1][:10]\n",
    "\n",
    "feature_names_neg = tfidf_neg.get_feature_names_out()\n",
    "bigram_keywords_neg = [feature_names_neg[idx] for idx in top_indices_neg]\n",
    "\n",
    "print(\"Ключевые биграммы для текстов с негативным настроем:\", bigram_keywords_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4e88ca0f-a477-402f-a13d-29e2dfdd363e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>сотрудник банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>кредитный карта</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>денежный средство</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>отделение банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>написать заявление</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>горячий линия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ваш банк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>колл центр</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>рабочий день</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>альфа банк</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0\n",
       "0      сотрудник банк\n",
       "1     кредитный карта\n",
       "2   денежный средство\n",
       "3      отделение банк\n",
       "4  написать заявление\n",
       "5       горячий линия\n",
       "6            ваш банк\n",
       "7          колл центр\n",
       "8        рабочий день\n",
       "9          альфа банк"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(bigram_keywords_neg[0]).T[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c17e71af-0055-414a-ae6b-0952cc25821f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bed952e-6c17-4e7a-97d5-760de236d9a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "54458e75-0cdb-4263-879f-a1937aaf92c2",
   "metadata": {},
   "source": [
    "### Тематическое моделирование"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2a04ed8d-be29-4a69-b430-ffd15ed322fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a402448d-a30c-4aff-ad39-6dc2294c2740",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import TfidfModel\n",
    "from gensim import similarities\n",
    "from gensim.models import lsimodel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f27283-4b8f-463e-89aa-ce729ec7f34c",
   "metadata": {},
   "source": [
    "##### 2.1. Постройте несколько тематических моделей коллекции документов с разным числом тем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6433417d-2dd4-49e2-bf8d-78e7fb2d45f0",
   "metadata": {
    "executionInfo": {
     "elapsed": 1342,
     "status": "ok",
     "timestamp": 1664627452390,
     "user": {
      "displayName": "Артур Сапрыкин",
      "userId": "16185371824692237125"
     },
     "user_tz": -180
    },
    "id": "suiWngD5IVaS"
   },
   "outputs": [],
   "source": [
    "texts = [text.split() for text in df.text]\n",
    "dictionary = Dictionary(texts)\n",
    "corpus = [dictionary.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "81ff1fae-e425-4d6f-8358-bbe0ebb499c4",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1664627452390,
     "user": {
      "displayName": "Артур Сапрыкин",
      "userId": "16185371824692237125"
     },
     "user_tz": -180
    },
    "id": "iqRhGGiMIVaU"
   },
   "outputs": [],
   "source": [
    "tfidf = TfidfModel(corpus)\n",
    "corpus_tfidf = tfidf[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d020dacb-bee7-4d5a-85df-f6c2dad807fb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6807,
     "status": "ok",
     "timestamp": 1664627487483,
     "user": {
      "displayName": "Артур Сапрыкин",
      "userId": "16185371824692237125"
     },
     "user_tz": -180
    },
    "id": "B1-mIFauIVaV",
    "outputId": "99d0a688-2523-4d89-aee1-5c6f725b02c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 35s\n",
      "Wall time: 1min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lsi = lsimodel.LsiModel(corpus=corpus_tfidf, id2word=dictionary, num_topics=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "32de8d71-0b53-4391-a934-0e1fd2142c16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.192*\"карта\" + 0.131*\"кредит\" + 0.114*\"деньга\" + 0.108*\"заявление\" + 0.106*\"счёт\" + 0.100*\"вклад\" + 0.099*\"банкомат\" + 0.099*\"отделение\" + 0.096*\"сбербанк\" + 0.095*\"сумма\"'),\n",
       " (1,\n",
       "  '-0.447*\"вклад\" + 0.341*\"кредит\" + -0.169*\"банкомат\" + 0.155*\"платёж\" + 0.154*\"страховка\" + 0.149*\"задолженность\" + -0.139*\"карта\" + 0.138*\"погашение\" + -0.119*\"очередь\" + -0.113*\"открыть\"'),\n",
       " (2,\n",
       "  '-0.444*\"вклад\" + 0.283*\"банкомат\" + 0.240*\"карта\" + 0.208*\"сбербанк\" + -0.180*\"кредит\" + -0.134*\"договор\" + 0.134*\"операция\" + 0.130*\"заявление\" + 0.126*\"деньга\" + 0.123*\"средство\"'),\n",
       " (3,\n",
       "  '-0.276*\"вклад\" + -0.216*\"сумма\" + 0.192*\"номер\" + 0.191*\"заявка\" + -0.187*\"руб\" + -0.156*\"платёж\" + -0.155*\"договор\" + -0.154*\"погашение\" + -0.146*\"рубль\" + 0.145*\"звонок\"'),\n",
       " (4,\n",
       "  '-0.445*\"карта\" + 0.265*\"банкомат\" + -0.197*\"лимит\" + -0.184*\"кредитный\" + 0.172*\"номер\" + 0.142*\"вклад\" + 0.129*\"звонок\" + 0.119*\"терминал\" + 0.118*\"деньга\" + 0.112*\"купюра\"'),\n",
       " (5,\n",
       "  '-0.341*\"банкомат\" + 0.277*\"вклад\" + -0.239*\"страховка\" + -0.188*\"кредит\" + 0.180*\"номер\" + -0.141*\"заявка\" + 0.140*\"звонок\" + 0.111*\"средство\" + 0.110*\"задолженность\" + 0.108*\"ваш\"'),\n",
       " (6,\n",
       "  '0.259*\"сбербанк\" + 0.204*\"заявка\" + 0.202*\"заявление\" + 0.194*\"страховка\" + -0.191*\"платёж\" + 0.169*\"страхование\" + 0.162*\"документ\" + 0.138*\"денежный\" + -0.138*\"задолженность\" + 0.137*\"рассмотрение\"'),\n",
       " (7,\n",
       "  '0.258*\"вклад\" + 0.248*\"карта\" + -0.229*\"перевод\" + -0.208*\"кошелёк\" + 0.200*\"заявление\" + -0.164*\"киви\" + -0.152*\"комиссия\" + -0.137*\"курс\" + 0.125*\"задолженность\" + -0.125*\"страховка\"'),\n",
       " (8,\n",
       "  '0.428*\"страховка\" + 0.262*\"банкомат\" + 0.226*\"страхование\" + -0.211*\"платёж\" + 0.191*\"номер\" + 0.153*\"страховой\" + 0.122*\"звонок\" + -0.122*\"справка\" + -0.118*\"документ\" + -0.116*\"кошелёк\"'),\n",
       " (9,\n",
       "  '-0.479*\"кошелёк\" + -0.351*\"киви\" + -0.215*\"вклад\" + 0.195*\"сбербанк\" + -0.148*\"идентификация\" + -0.148*\"страховка\" + -0.148*\"заявка\" + -0.144*\"поддержка\" + -0.134*\"терминал\" + -0.118*\"служба\"')]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsi.show_topics(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cc2d822-59b8-4ce5-9452-8803f9b8465b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f7151e71-7159-4157-a958-f90c972e8a34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 21s\n",
      "Wall time: 1min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lsi_5 = lsimodel.LsiModel(corpus=corpus_tfidf, id2word=dictionary, num_topics=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b14fa7f9-6ae1-4e8d-ad9d-4bcc2d7f80a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.192*\"карта\" + 0.132*\"кредит\" + 0.113*\"деньга\" + 0.108*\"заявление\" + 0.105*\"счёт\" + 0.099*\"вклад\" + 0.099*\"банкомат\" + 0.099*\"отделение\" + 0.095*\"сбербанк\" + 0.095*\"сумма\"'),\n",
       " (1,\n",
       "  '-0.479*\"вклад\" + 0.327*\"кредит\" + 0.161*\"страховка\" + 0.146*\"платёж\" + 0.145*\"задолженность\" + 0.134*\"погашение\" + -0.130*\"очередь\" + -0.128*\"банкомат\" + -0.120*\"открыть\" + -0.119*\"карта\"'),\n",
       " (2,\n",
       "  '-0.392*\"вклад\" + 0.267*\"карта\" + 0.263*\"банкомат\" + -0.215*\"кредит\" + 0.203*\"сбербанк\" + -0.148*\"страховка\" + 0.141*\"операция\" + -0.136*\"договор\" + 0.129*\"средство\" + 0.125*\"деньга\"'),\n",
       " (3,\n",
       "  '-0.273*\"вклад\" + -0.211*\"сумма\" + 0.204*\"номер\" + 0.194*\"заявка\" + -0.182*\"руб\" + -0.167*\"договор\" + 0.163*\"звонок\" + -0.152*\"платёж\" + -0.146*\"погашение\" + -0.145*\"средство\"'),\n",
       " (4,\n",
       "  '0.290*\"вклад\" + 0.274*\"номер\" + 0.231*\"звонок\" + -0.213*\"страховка\" + -0.192*\"заявка\" + 0.168*\"ваш\" + 0.147*\"поступать\" + 0.141*\"персональный\" + 0.138*\"данные\" + 0.131*\"база\"')]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsi_5.show_topics(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1d70fa66-c560-408f-a8c9-910220d0dd56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 22s\n",
      "Wall time: 1min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lsi_3 = lsimodel.LsiModel(corpus=corpus_tfidf, id2word=dictionary, num_topics=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "40f7093d-0874-4310-8983-5f09e0c50b26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.192*\"карта\" + 0.132*\"кредит\" + 0.113*\"деньга\" + 0.108*\"заявление\" + 0.105*\"счёт\" + 0.099*\"банкомат\" + 0.099*\"отделение\" + 0.099*\"вклад\" + 0.096*\"сбербанк\" + 0.095*\"сумма\"'),\n",
       " (1,\n",
       "  '-0.565*\"вклад\" + 0.232*\"кредит\" + -0.151*\"очередь\" + 0.139*\"номер\" + 0.135*\"задолженность\" + -0.134*\"открыть\" + 0.134*\"звонок\" + 0.129*\"платёж\" + 0.103*\"погашение\" + 0.102*\"ваш\"'),\n",
       " (2,\n",
       "  '0.385*\"банкомат\" + -0.245*\"вклад\" + 0.225*\"карта\" + -0.201*\"кредит\" + 0.199*\"сбербанк\" + 0.174*\"деньга\" + -0.156*\"звонок\" + 0.146*\"операция\" + 0.128*\"средство\" + 0.119*\"заявление\"')]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsi_3.show_topics(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fbb98bf-877a-4a36-b53a-c80295618c29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e9acc8b9-c201-42f0-849e-328a7d9f5391",
   "metadata": {},
   "source": [
    "##### 2.2. Приведите примеры понятных (интерпретируемых) тем"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a138a06d-67ae-43fc-bb5f-59327d830bf1",
   "metadata": {},
   "source": [
    "#### Интерпретируемые темы:  \n",
    "* Тема 0 интерпретируется как получение кредитной карты\n",
    "* Тема 1 интерпретируется как погашение задоженности по кредиту\n",
    "* Тема 2 интерпретируется как операция с картой в банкоматах сбербанка"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16d617b-d06a-404a-ab54-42c547d98518",
   "metadata": {},
   "source": [
    "##### 2.3. Найдите темы, в которых упомянуты конкретные банки (Сбербанк, ВТБ, другой банк). Можете ли вы их прокомментировать / объяснить?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7620137a-1fd3-4229-aa25-f5f6275a9805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 44s\n",
      "Wall time: 1min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lsi_25 = lsimodel.LsiModel(corpus=corpus_tfidf, id2word=dictionary, num_topics=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bfda0a87-bd40-46b8-9bdb-d0c493a67ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.192*\"карта\" + 0.131*\"кредит\" + 0.113*\"деньга\" + 0.108*\"заявление\" + 0.105*\"счёт\" + 0.100*\"банкомат\" + 0.099*\"вклад\" + 0.099*\"отделение\" + 0.095*\"сбербанк\" + 0.095*\"сумма\"'),\n",
       " (1,\n",
       "  '-0.392*\"вклад\" + 0.353*\"кредит\" + -0.206*\"банкомат\" + -0.167*\"карта\" + 0.163*\"страховка\" + 0.147*\"задолженность\" + 0.144*\"платёж\" + 0.138*\"погашение\" + -0.117*\"деньга\" + 0.117*\"звонок\"'),\n",
       " (2,\n",
       "  '-0.488*\"вклад\" + 0.282*\"банкомат\" + 0.222*\"карта\" + 0.186*\"сбербанк\" + -0.138*\"кредит\" + 0.135*\"заявление\" + 0.123*\"операция\" + 0.121*\"деньга\" + -0.120*\"договор\" + 0.118*\"средство\"'),\n",
       " (3,\n",
       "  '-0.267*\"вклад\" + -0.220*\"сумма\" + 0.198*\"номер\" + -0.192*\"руб\" + 0.168*\"заявка\" + -0.159*\"погашение\" + -0.156*\"договор\" + -0.156*\"платёж\" + 0.151*\"звонок\" + -0.146*\"процент\"'),\n",
       " (4,\n",
       "  '0.327*\"вклад\" + -0.271*\"карта\" + 0.261*\"номер\" + -0.207*\"страховка\" + -0.192*\"заявка\" + 0.185*\"звонок\" + -0.142*\"кредит\" + 0.137*\"ваш\" + 0.133*\"телефон\" + -0.131*\"лимит\"'),\n",
       " (5,\n",
       "  '0.475*\"банкомат\" + -0.359*\"карта\" + 0.159*\"купюра\" + 0.142*\"деньга\" + -0.137*\"лимит\" + -0.135*\"кредитный\" + 0.129*\"кредит\" + -0.119*\"смс\" + 0.117*\"чек\" + 0.111*\"очередь\"'),\n",
       " (6,\n",
       "  '0.249*\"заявка\" + 0.244*\"сбербанк\" + 0.209*\"заявление\" + -0.193*\"платёж\" + 0.184*\"страховка\" + 0.149*\"документ\" + 0.147*\"рассмотрение\" + 0.144*\"страхование\" + 0.139*\"вклад\" + -0.139*\"задолженность\"'),\n",
       " (7,\n",
       "  '-0.293*\"вклад\" + 0.279*\"перевод\" + 0.273*\"сбербанк\" + -0.248*\"карта\" + -0.245*\"банкомат\" + 0.160*\"платёж\" + 0.121*\"терминал\" + 0.117*\"счёт\" + -0.113*\"звонок\" + -0.112*\"задолженность\"'),\n",
       " (8,\n",
       "  '0.300*\"банкомат\" + 0.229*\"страховка\" + -0.210*\"платёж\" + -0.201*\"сбербанк\" + 0.174*\"страхование\" + 0.166*\"альфа\" + 0.138*\"комиссия\" + 0.135*\"услуга\" + -0.118*\"погашение\" + 0.108*\"ваш\"'),\n",
       " (9,\n",
       "  '-0.384*\"страховка\" + -0.322*\"сбербанк\" + 0.269*\"заявка\" + -0.184*\"страхование\" + -0.146*\"номер\" + 0.142*\"документ\" + -0.128*\"страховой\" + 0.128*\"рассмотрение\" + -0.120*\"карта\" + -0.110*\"смс\"'),\n",
       " (10,\n",
       "  '-0.310*\"заявка\" + -0.182*\"страховка\" + 0.181*\"карта\" + -0.180*\"смс\" + -0.177*\"вклад\" + -0.165*\"кошелёк\" + 0.163*\"заявление\" + -0.152*\"номер\" + -0.142*\"киви\" + 0.131*\"отделение\"'),\n",
       " (11,\n",
       "  '0.459*\"сбербанк\" + -0.232*\"перевод\" + -0.227*\"счёт\" + -0.186*\"страховка\" + -0.155*\"заявление\" + 0.142*\"онлайн\" + 0.141*\"заявка\" + 0.130*\"вклад\" + 0.129*\"кредит\" + 0.126*\"терминал\"'),\n",
       " (12,\n",
       "  '-0.304*\"сбербанк\" + 0.283*\"терминал\" + 0.203*\"платёж\" + -0.197*\"комиссия\" + -0.188*\"перевод\" + 0.168*\"кошелёк\" + 0.144*\"киви\" + 0.130*\"очередь\" + -0.120*\"счёт\" + -0.117*\"деньга\"'),\n",
       " (13,\n",
       "  '0.340*\"кошелёк\" + 0.265*\"киви\" + 0.257*\"терминал\" + -0.191*\"смс\" + -0.172*\"погашение\" + -0.152*\"альфа\" + -0.149*\"досрочный\" + -0.134*\"претензия\" + -0.132*\"услуга\" + 0.108*\"карта\"'),\n",
       " (14,\n",
       "  '0.379*\"руб\" + 0.242*\"терминал\" + 0.209*\"комиссия\" + -0.206*\"альфа\" + 0.186*\"заявка\" + 0.154*\"очередь\" + 0.146*\"касса\" + 0.137*\"лимит\" + -0.124*\"банкомат\" + 0.122*\"кассир\"'),\n",
       " (15,\n",
       "  '-0.323*\"перевод\" + 0.283*\"смс\" + 0.232*\"альфа\" + 0.197*\"руб\" + 0.184*\"претензия\" + -0.170*\"страховка\" + 0.164*\"справка\" + 0.163*\"документ\" + -0.151*\"платёж\" + 0.148*\"услуга\"'),\n",
       " (16,\n",
       "  '0.316*\"кредит\" + 0.278*\"справка\" + 0.250*\"кошелёк\" + 0.190*\"киви\" + 0.171*\"номер\" + -0.153*\"платёж\" + 0.151*\"счёт\" + 0.151*\"погашение\" + -0.146*\"страховка\" + 0.139*\"перевод\"'),\n",
       " (17,\n",
       "  '-0.454*\"альфа\" + -0.336*\"терминал\" + -0.223*\"претензия\" + 0.217*\"банкомат\" + 0.181*\"кошелёк\" + 0.171*\"досрочный\" + 0.167*\"погашение\" + -0.160*\"счёт\" + -0.152*\"кредитный\" + 0.132*\"руб\"'),\n",
       " (18,\n",
       "  '0.264*\"договор\" + -0.253*\"средство\" + -0.239*\"кошелёк\" + -0.224*\"денежный\" + 0.190*\"терминал\" + 0.188*\"комиссия\" + -0.175*\"заблокировать\" + -0.158*\"страховка\" + -0.147*\"киви\" + 0.136*\"справка\"'),\n",
       " (19,\n",
       "  '0.291*\"терминал\" + -0.235*\"смс\" + 0.213*\"курс\" + 0.192*\"погашение\" + 0.186*\"досрочный\" + -0.163*\"банкомат\" + -0.159*\"платёж\" + -0.136*\"договор\" + 0.128*\"номер\" + -0.126*\"просрочка\"'),\n",
       " (20,\n",
       "  '0.404*\"руб\" + -0.385*\"курс\" + -0.220*\"валюта\" + -0.214*\"доллар\" + -0.214*\"евро\" + -0.194*\"рубль\" + 0.171*\"комиссия\" + -0.110*\"персональный\" + -0.103*\"конвертация\" + 0.100*\"тыс\"'),\n",
       " (21,\n",
       "  '0.270*\"руб\" + -0.194*\"заявление\" + 0.186*\"сбербанк\" + -0.183*\"средство\" + 0.182*\"справка\" + -0.180*\"р\" + -0.176*\"смс\" + 0.172*\"альфа\" + 0.170*\"претензия\" + -0.158*\"денежный\"'),\n",
       " (22,\n",
       "  '-0.245*\"р\" + -0.240*\"альфа\" + 0.239*\"курс\" + 0.216*\"заявление\" + -0.170*\"сч\" + 0.167*\"смс\" + -0.164*\"сбербанк\" + 0.152*\"ваш\" + 0.151*\"руб\" + -0.150*\"претензия\"'),\n",
       " (23,\n",
       "  '-0.236*\"персональный\" + 0.234*\"договор\" + -0.226*\"страховка\" + -0.193*\"сч\" + -0.177*\"данные\" + -0.176*\"обработка\" + -0.147*\"р\" + -0.141*\"вс\" + 0.139*\"комиссия\" + -0.135*\"согласие\"'),\n",
       " (24,\n",
       "  '0.386*\"р\" + -0.333*\"руб\" + -0.195*\"альфа\" + 0.167*\"справка\" + -0.165*\"персональный\" + 0.146*\"горячий\" + 0.145*\"линия\" + 0.140*\"страховка\" + -0.129*\"данные\" + 0.128*\"смс\"')]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsi_25.show_topics(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc8ea07-2c65-4ea3-9896-d00b9a10895a",
   "metadata": {},
   "source": [
    "#### Темы с упоминаемыми банками:  \n",
    "* Тема 2 по операциям с картой в банкоматах сбербанка. Не удивительно в связи с большой долей сбербанка в России, поэтому много вопросов.\n",
    "* Тема 13 по операциям через терминал с кошельком Киви. В своё время распространённая денежная система.\n",
    "* Тема 17 по операциям через терминал альфа-банка по кредитной карте. Были какие-то претензии."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecb048e-00f8-4868-aabd-a27e5f6d1527",
   "metadata": {},
   "source": [
    "### Классификация текстов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c5adc3-e36a-4350-a35e-095501375e14",
   "metadata": {},
   "source": [
    "##### Будем классифицировать на два класса, различать резко отрицательные отзывы (с оценкой 1) и положительные отзывы (с оценкой 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38162adc-cfa5-4a09-9416-abb7468e46b4",
   "metadata": {},
   "source": [
    "##### 3.1. Составьте обучающее и тестовое множество: выберите из всего набора данных N1 отзывов с оценкой 1 и N2 отзывов с оценкой 5. Используйте sklearn.model_selection.train_test_split для разделения множества отобранных документов на обучающее и тестовое"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "74e2521e-ad33-49a2-9cd6-a471622e2b9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число строк с позитивным отзывом = 14713,\n",
      "Число строк с негативным отзывом = 47387\n"
     ]
    }
   ],
   "source": [
    "print(f'Число строк с позитивным отзывом = {df_positive.shape[0]},\\n\\\n",
    "Число строк с негативным отзывом = {df_negative.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3d23db77-33e9-400e-8cf2-ed6ad4fb2e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "N1, N2 = 7000, 23000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "613c23ae-569b-498f-8ae1-674d5fdd2525",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cl = pd.concat([df_positive[:N1],df_negative[:N2]], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "22487acc-54d8-4f20-beb6-61b511d308e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6bbc3ac3-1ec1-4e04-b011-03a6ca53743f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>bank_name</th>\n",
       "      <th>rating_grade</th>\n",
       "      <th>text</th>\n",
       "      <th>text_only_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>г. Санкт-Петербург</td>\n",
       "      <td>Русславбанк</td>\n",
       "      <td>5.0</td>\n",
       "      <td>досрочно закрывать вклад отпуск жена заказыват...</td>\n",
       "      <td>[досрочно, закрывали, вклады, перед, отпуском,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>г. Нижний Новгород</td>\n",
       "      <td>РосинтерБанк</td>\n",
       "      <td>5.0</td>\n",
       "      <td>хотеть открыть вклад доходный год процент начи...</td>\n",
       "      <td>[хотел, открыть, вклад, доходный, под, год, пр...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>ВТБ 24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>вчера июнь просидеть очередь х час получение с...</td>\n",
       "      <td>[вчера, июня, просидев, в, очереди, более, х, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>Соверен Банк</td>\n",
       "      <td>5.0</td>\n",
       "      <td>сегодня открывать вклад банк очередь один клие...</td>\n",
       "      <td>[сегодня, открывал, вклад, в, этом, банке, оче...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>г. Москва</td>\n",
       "      <td>Ренессанс Кредит</td>\n",
       "      <td>5.0</td>\n",
       "      <td>история получение кредит ренессанс кредит банк...</td>\n",
       "      <td>[моя, история, получения, кредита, в, ренессан...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city         bank_name  rating_grade  \\\n",
       "0  г. Санкт-Петербург       Русславбанк           5.0   \n",
       "1  г. Нижний Новгород      РосинтерБанк           5.0   \n",
       "2           г. Москва            ВТБ 24           5.0   \n",
       "3           г. Москва      Соверен Банк           5.0   \n",
       "4           г. Москва  Ренессанс Кредит           5.0   \n",
       "\n",
       "                                                text  \\\n",
       "0  досрочно закрывать вклад отпуск жена заказыват...   \n",
       "1  хотеть открыть вклад доходный год процент начи...   \n",
       "2  вчера июнь просидеть очередь х час получение с...   \n",
       "3  сегодня открывать вклад банк очередь один клие...   \n",
       "4  история получение кредит ренессанс кредит банк...   \n",
       "\n",
       "                                     text_only_words  \n",
       "0  [досрочно, закрывали, вклады, перед, отпуском,...  \n",
       "1  [хотел, открыть, вклад, доходный, под, год, пр...  \n",
       "2  [вчера, июня, просидев, в, очереди, более, х, ...  \n",
       "3  [сегодня, открывал, вклад, в, этом, банке, оче...  \n",
       "4  [моя, история, получения, кредита, в, ренессан...  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8b64f196-fc0a-4c9e-930f-436e783237ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_cl.text, df_cl.rating_grade,\\\n",
    "                                                    test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b841f5-2873-4e98-887f-c0c18e7d95cb",
   "metadata": {},
   "source": [
    "##### 3.2. Используйте любой известный вам алгоритм классификации текстов для решения задачи и получите baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c17c868c-37cc-4aea-9fbc-8e95d5ccea2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "37ecdc4f-11e9-4c51-8d9b-da1802718235",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       1.00      0.84      0.91      5517\n",
      "         5.0       0.35      1.00      0.52       483\n",
      "\n",
      "    accuracy                           0.85      6000\n",
      "   macro avg       0.68      0.92      0.72      6000\n",
      "weighted avg       0.95      0.85      0.88      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "clf = MultinomialNB()\n",
    "clf.fit(X_train_tfidf, y_train)\n",
    "\n",
    "pred = clf.predict(X_test_tfidf)\n",
    "print(classification_report(pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "906d4e53-7e8f-497c-be05-2604d880c9f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ee586959-cf38-4d7c-9f29-ec15e2aed57d",
   "metadata": {},
   "source": [
    "##### 3.3. Сравните разные варианты векторизации текста: использование только униграм, пар или троек слов или с использованием символьных $n$-грам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7fa75a6d-d235-4fcf-8bd4-bb68381e8741",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = CountVectorizer(ngram_range=(1, 1))\n",
    "bow = vec.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3a1b1574-b096-43d5-a407-0b83b129a25d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse._csr.csr_matrix"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "bf018a07-1017-4873-aa51-d14188d1d4b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=42, solver='liblinear')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=42, solver='liblinear')\n",
    "clf.fit(bow, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "037fd816-a2f5-4940-8706-17d3815d56b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.98      0.97      0.98      4659\n",
      "         5.0       0.91      0.93      0.92      1341\n",
      "\n",
      "    accuracy                           0.96      6000\n",
      "   macro avg       0.94      0.95      0.95      6000\n",
      "weighted avg       0.96      0.96      0.96      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = clf.predict(vec.transform(X_test))\n",
    "print(classification_report(pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6239533a-d8b4-46bf-a27b-0240c9a25422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.99      0.94      0.96      4889\n",
      "         5.0       0.78      0.97      0.87      1111\n",
      "\n",
      "    accuracy                           0.94      6000\n",
      "   macro avg       0.89      0.95      0.92      6000\n",
      "weighted avg       0.95      0.94      0.95      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vec = CountVectorizer(ngram_range=(2, 2))\n",
    "bow = vec.fit_transform(X_train)\n",
    "clf = LogisticRegression(random_state=42)\n",
    "clf.fit(bow, y_train)\n",
    "pred = clf.predict(vec.transform(X_test))\n",
    "print(classification_report(pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "935bc3c5-f45a-4dae-b8cc-e80fcae77e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       1.00      0.82      0.90      5639\n",
      "         5.0       0.26      0.97      0.40       361\n",
      "\n",
      "    accuracy                           0.83      6000\n",
      "   macro avg       0.63      0.90      0.65      6000\n",
      "weighted avg       0.95      0.83      0.87      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vec = CountVectorizer(ngram_range=(3, 3))\n",
    "bow = vec.fit_transform(X_train)\n",
    "clf = LogisticRegression(random_state=42)\n",
    "clf.fit(bow, y_train)\n",
    "pred = clf.predict(vec.transform(X_test))\n",
    "print(classification_report(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ab70c8-4f53-49f7-9e4c-8dfdbaafa082",
   "metadata": {},
   "source": [
    "##### 3.4. Сравните, как изменяется качество решения задачи при использовании скрытых тем в качестве признаков:\n",
    "1-ый вариант: \n",
    " преобразование (sklearn.feature_extraction.text.TfidfTransformer) и сингулярное разложение (оно же – латентый семантический анализ) (sklearn.decomposition.TruncatedSVD),  \n",
    "2-ой вариант: тематические модели LDA (sklearn.decomposition.LatentDirichletAllocation)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ecdc9d-f7cb-4f2b-a4d9-45dcf029657d",
   "metadata": {},
   "source": [
    "##### 3.5. Используйте accuracy и F-measure для оценки качества классификации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "121092f6-4efe-4b67-9a94-6a61736a81cd",
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1664627496827,
     "user": {
      "displayName": "Артур Сапрыкин",
      "userId": "16185371824692237125"
     },
     "user_tz": -180
    },
    "id": "YhKQI-oWIVaX"
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f5841e-8ec6-4550-a7dd-1bd1201de093",
   "metadata": {},
   "source": [
    "###### 1-й вариант"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4e276bba-30f0-4f0f-be14-b9efb84213a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "811c0b35-476c-4b54-aaa5-8ce860661d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tfidf = vec.fit_transform(X_train)\n",
    "X_test_tfidf = vec.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "dcb8e587-e382-4801-a737-9630891c90ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "lsa = TruncatedSVD(n_components=20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5023d7d1-c740-4288-bc7f-286120ec1125",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_lsa = lsa.fit_transform(X_train_tfidf)\n",
    "X_test_lsa = lsa.transform(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c486f712-1fe2-4dc0-991d-1220f4d4741c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=42) #MultinomialNB()\n",
    "clf.fit(X_train_lsa, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "462cf62b-0f3f-4c2b-b47b-33ad1280ec4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.97      0.93      0.95      4864\n",
      "         5.0       0.74      0.90      0.81      1136\n",
      "\n",
      "    accuracy                           0.92      6000\n",
      "   macro avg       0.86      0.91      0.88      6000\n",
      "weighted avg       0.93      0.92      0.92      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = clf.predict(X_test_lsa)\n",
    "print(classification_report(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ba5b7b-f29c-489a-9a73-9c4cadd5e140",
   "metadata": {},
   "source": [
    "###### 2-й вариант"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3256caae-cac3-43b4-89af-1ff57acf1f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = CountVectorizer()\n",
    "X_train_counts = vec.fit_transform(X_train)\n",
    "X_test_counts = vec.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "167911ae-bc4c-44fd-81a4-28015cbf5bcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LatentDirichletAllocation(n_components = 25, random_state = 42)\n",
    "X_train_lda = lda.fit_transform(X_train_counts)\n",
    "X_test_lda = lda.transform(X_test_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b23cfbc6-1000-4c3a-8c74-f533d7945f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=42) #MultinomialNB()\n",
    "clf.fit(X_train_lda, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "487656a9-4911-4496-822a-d9326e407e16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.98      0.94      0.96      4799\n",
      "         5.0       0.80      0.91      0.85      1201\n",
      "\n",
      "    accuracy                           0.94      6000\n",
      "   macro avg       0.89      0.93      0.91      6000\n",
      "weighted avg       0.94      0.94      0.94      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = clf.predict(X_test_lda)\n",
    "print(classification_report(pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e92d9361-5480-4766-a151-4c0f19f04301",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end_time = time.time()\n",
    "#execution_time = end_time - start_time\n",
    "#print(f\"Total execution time: {.2} seconds\".format(execution_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "018f7235-4f4a-433a-ac1d-44bbe89ca084",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62f8a884-d58a-45e0-b14b-3394bb670eee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae6edf1-ecf3-401e-b099-fccc1aee3a76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "189f8c75-8986-448d-8da2-edc5b35590de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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