sentiment_analysis_final_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sentiment analysis final.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JUPUDI-VAMSI-KALYAN/Data_Science-Python-/blob/master/sentiment_analysis_final_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrcMLS2i_FOt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xR9JaDHY_OhO",
        "colab_type": "code",
        "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": 144
        },
        "outputId": "b7c31028-47e7-408e-8adc-a8f6d0555bec"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-23550a1f-4d30-41e7-b970-3c42abe3db91\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-23550a1f-4d30-41e7-b970-3c42abe3db91\">\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 amazon_cells_labelled.txt to amazon_cells_labelled.txt\n",
            "Saving imdb_labelled.txt to imdb_labelled.txt\n",
            "Saving yelp_labelled.txt to yelp_labelled.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tb5VJc7a_OlL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pickle\n",
        "import sys\n",
        "import os\n",
        "import io\n",
        "import re\n",
        "from sys import path\n",
        "import numpy as np\n",
        "import pickle\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "import matplotlib.pyplot as plt\n",
        "from string import punctuation, digits\n",
        "from IPython.core.display import display, HTML\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.tokenize import RegexpTokenizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rME1wdTt_Ofl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "amazon = pd.read_csv('amazon_cells_labelled.txt',delimiter='\\t',header=None)\n",
        "amazon.columns = ['Sentence','Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tkfWZGR_yyd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yelp = pd.read_csv('yelp_labelled.txt',delimiter='\\t',header=None)\n",
        "yelp.columns = ['Sentence','Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_dqs1BX_ytu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "outputId": "e43dc483-8582-4dd3-9ebf-8b34ccb029c6"
      },
      "source": [
        "imdb = pd.read_csv('imdb_labelled.txt',delimiter='\\t',header=None)\n",
        "imdb.columns = ['Sentence','Class']\n",
        "import nltk\n",
        "nltk.download('popular')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading collection 'popular'\n",
            "[nltk_data]    | \n",
            "[nltk_data]    | Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/cmudict.zip.\n",
            "[nltk_data]    | Downloading package gazetteers to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gazetteers.zip.\n",
            "[nltk_data]    | Downloading package genesis to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/genesis.zip.\n",
            "[nltk_data]    | Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gutenberg.zip.\n",
            "[nltk_data]    | Downloading package inaugural to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/inaugural.zip.\n",
            "[nltk_data]    | Downloading package movie_reviews to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/movie_reviews.zip.\n",
            "[nltk_data]    | Downloading package names to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/names.zip.\n",
            "[nltk_data]    | Downloading package shakespeare to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/shakespeare.zip.\n",
            "[nltk_data]    | Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data]    | Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/treebank.zip.\n",
            "[nltk_data]    | Downloading package twitter_samples to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/twitter_samples.zip.\n",
            "[nltk_data]    | Downloading package omw to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/omw.zip.\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet_ic.zip.\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/words.zip.\n",
            "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping chunkers/maxent_ne_chunker.zip.\n",
            "[nltk_data]    | Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data]    | Downloading package snowball_data to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection popular\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fdFLZTm_ytB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "6e7c4f73-4e75-4f08-bc3c-f76431d287d6"
      },
      "source": [
        "#combine all data sets\n",
        "data = pd.DataFrame()\n",
        "data = pd.concat([amazon, yelp, imdb])\n",
        "data['index'] = data.index\n",
        "\n",
        "data\n",
        "\n",
        "#Total Count of Each Category\n",
        "pd.set_option('display.width', 4000)\n",
        "pd.set_option('display.max_rows', 1000)\n",
        "distOfDetails = data.groupby(by='Class', as_index=False).agg({'index': pd.Series.nunique}).sort_values(by='index', ascending=False)\n",
        "distOfDetails.columns =['Class', 'COUNT']\n",
        "print(distOfDetails)\n",
        "#Distribution of All Categories\n",
        "plt.pie(distOfDetails['COUNT'],autopct='%1.0f%%',shadow=True, startangle=360)\n",
        "plt.show()\n",
        "\n",
        "#Text Preprocessing\n",
        "columns = ['index','Class', 'Sentence']\n",
        "df_ = pd.DataFrame(columns=columns)\n",
        "#lower string\n",
        "data['Sentence'] = data['Sentence'].str.lower()\n",
        "#remove email adress\n",
        "data['Sentence'] = data['Sentence'].replace('[a-zA-Z0-9-_.]+@[a-zA-Z0-9-_.]+', '', regex=True)\n",
        "#remove IP address\n",
        "data['Sentence'] = data['Sentence'].replace('((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)(\\.|$)){4}', '', regex=True)\n",
        "#remove punctaitions and special chracters\n",
        "data['Sentence'] = data['Sentence'].str.replace('[^\\w\\s]','')\n",
        "#remove numbers\n",
        "data['Sentence'] = data['Sentence'].replace('\\d', '', regex=True)\n",
        "\n",
        "#remove stop words\n",
        "for index, row in data.iterrows():\n",
        "    word_tokens = word_tokenize(row['Sentence'])\n",
        "    filtered_sentence = [w for w in word_tokens if not w in stopwords.words('english')]\n",
        "    df_ = df_.append({\"index\": row['index'], \"Class\":  row['Class'],\"Sentence\": \" \".join(filtered_sentence[0:])}, ignore_index=True)\n",
        "data = df_\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data['Sentence'].values.astype('U'),data['Class'].values.astype('int32'), test_size=0.10, random_state=0)\n",
        "classes  = data['Class'].unique()\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "#grid search result\n",
        "vectorizer = TfidfVectorizer(analyzer='word',ngram_range=(1,2), max_features=50000,max_df=0.5,use_idf=True, norm='l2') \n",
        "counts = vectorizer.fit_transform(X_train)\n",
        "vocab = vectorizer.vocabulary_\n",
        "classifier = SGDClassifier(alpha=1e-05,max_iter=50,penalty='elasticnet')\n",
        "targets = y_train\n",
        "classifier = classifier.fit(counts, targets)\n",
        "example_counts = vectorizer.transform(X_test)\n",
        "predictions = classifier.predict(example_counts)\n",
        "\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "#Model Evaluation\n",
        "acc = accuracy_score(y_test, predictions, normalize=True)\n",
        "hit = precision_score(y_test, predictions, average=None,labels=classes)\n",
        "capture = recall_score(y_test, predictions, average=None,labels=classes)\n",
        "print('Model Accuracy:%.2f'%acc)\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Class  COUNT\n",
            "0      0    846\n",
            "1      1    827\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bvFWGMgixR0qpJ02Ob6qcrTvHf3Ft+m1DGYT4VFprB3jGZAYj5WxsdvqAZcCg+Wvcdd0p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\npxABJeUUIqCknEIElJRTiID6/+WMcv4sIup/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model Accuracy:0.82\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.85      0.83       139\n",
            "           1       0.84      0.79      0.81       136\n",
            "\n",
            "    accuracy                           0.82       275\n",
            "   macro avg       0.82      0.82      0.82       275\n",
            "weighted avg       0.82      0.82      0.82       275\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDGFeSi8_ypC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-4TV0Jp_yoC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1DYGS4e_yME",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x48t2jGV_yK6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}