Naver_sentiment_movie_corpus.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Naver sentiment movie corpus.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNLb+l/1+44E2p6TU/nzPOT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KORshinjoonghyeok/ML-Naver-sentiment-movie-corpus/blob/main/Naver_sentiment_movie_corpus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TsuMyjr9OJkt",
        "outputId": "76f9c6f1-ac6a-4f8c-a0c9-bbf20bd803c1",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload() # data 업로드"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b5fe2035-d996-4402-873b-5d0d71afb252\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b5fe2035-d996-4402-873b-5d0d71afb252\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving ratings.txt to ratings.txt\n",
            "Saving ratings_test.txt to ratings_test.txt\n",
            "Saving ratings_train.txt to ratings_train.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kr-vpMvMOekh"
      },
      "source": [
        "!pip install konlpy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOIyzthPPBKe",
        "outputId": "00a0d4de-2638-4102-8639-a19c8133c493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "train_df = pd.read_csv('ratings_train.txt', sep = '\\t')\n",
        "train_df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>document</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9976970</td>\n",
              "      <td>아 더빙.. 진짜 짜증나네요 목소리</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3819312</td>\n",
              "      <td>흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10265843</td>\n",
              "      <td>너무재밓었다그래서보는것을추천한다</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9045019</td>\n",
              "      <td>교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6483659</td>\n",
              "      <td>사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id                                           document  label\n",
              "0   9976970                                아 더빙.. 진짜 짜증나네요 목소리      0\n",
              "1   3819312                  흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나      1\n",
              "2  10265843                                  너무재밓었다그래서보는것을추천한다      0\n",
              "3   9045019                      교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정      0\n",
              "4   6483659  사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ...      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvhK_EaXSA_o",
        "outputId": "56bb24e7-dfcc-457e-9ca0-b17678721b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "#0=부정\n",
        "#1=긍정\n",
        "train_df['label'].value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    75173\n",
              "1    74827\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_pOY1P-SZCs"
      },
      "source": [
        "import re\n",
        "\n",
        "train_df = train_df.fillna(' ')\n",
        "#숫자를 공백으로 변경하는 정규식\n",
        "train_df['document'] = train_df['document'].apply( lambda x : re.sub(r'\\d+', ' ', x))\n",
        "#테스트 데이터 로딩및 정규화\n",
        "test_df = pd.read_csv('ratings_test.txt', sep = '\\t')\n",
        "test_df = test_df.fillna(' ')\n",
        "test_df['documnet'] = test_df['document'].apply( lambda x :re.sub(r'\\d+', ' ', x))\n",
        "#불필요한 id칼럼 drop\n",
        "train_df.drop('id', axis=1, inplace=True)\n",
        "test_df.drop('id', axis=1, inplace=True)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1GyA7uVTxfy",
        "outputId": "65450a9e-d572-4c39-fefa-7a63037b9700",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from konlpy.tag import Twitter\n",
        "\n",
        "twitter = Twitter()\n",
        "\n",
        "def tw_tokenizer(text):\n",
        "  #텍스트를 형태소 단어로 토크나이저후 리스트 형태로 리턴\n",
        "  tokens_ko = twitter.morphs(text)\n",
        "  return tokens_ko"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/konlpy/tag/_okt.py:16: UserWarning: \"Twitter\" has changed to \"Okt\" since KoNLPy v0.4.5.\n",
            "  warn('\"Twitter\" has changed to \"Okt\" since KoNLPy v0.4.5.')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8skY7gIUTLY",
        "outputId": "8d5204e8-02e8-444c-8695-ad4cdca8a24e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "#Twitter객체의 morphs()객체를 이용한 tokenizer. ngram_range(1,2)\n",
        "tfidf_vect = TfidfVectorizer(tokenizer=tw_tokenizer, ngram_range=(1,2), min_df=3, max_df=0.9)\n",
        "tfidf_vect.fit(train_df['document'])\n",
        "tfidf_matrix_train = tfidf_vect.transform(train_df['document'])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:507: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\"The parameter 'token_pattern' will not be used\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90BpwrjvVZa5"
      },
      "source": [
        "#로지스틱 회귀 이용 감성분석 분류\n",
        "lg_clf = LogisticRegression(random_state=0)\n",
        "\n",
        "#파라미터 최적화 GridSearchCV\n",
        "params = {'C': [1,3.5,4,5.5,10]}"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1oJK5-GnwGS"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zn9tnY_0hD9b",
        "outputId": "d76eae8f-ab2c-4ab1-d7f8-6215a31c8f63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "grid_cv = GridSearchCV(lg_clf, param_grid=params, cv=3, scoring='accuracy', verbose=1)\n",
        "grid_cv.fit(tfidf_matrix_train, train_df['label'])\n",
        "print(grid_cv.best_params_, round(grid_cv.best_score_,4))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  1.2min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'C': 3.5} 0.8593\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0c0JK4s8hkiP",
        "outputId": "f38ef78f-99f4-4446-92a4-dede06848b1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "#학습 데이터를 적용한 TfidVectorizer를 이용 테스트 데이터를 TF-IDF값으로 피쳐변환\n",
        "tfidf_matrix_test = tfidf_vect.transform(test_df['document'])\n",
        "\n",
        "#best params이용\n",
        "best_estimator = grid_cv.best_estimator_\n",
        "pred = best_estimator.predict(tfidf_matrix_test)\n",
        "\n",
        "print('Logistic Regression 정확도: ', accuracy_score(test_df['label'],pred))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression 정확도:  0.86102\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8S3zVciho8NF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}