NLP Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries and Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "from jupyterthemes import jtplot\n",
    "\n",
    "jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df= pd.read_csv('twitter.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31957</th>\n",
       "      <td>31958</td>\n",
       "      <td>0</td>\n",
       "      <td>ate @user isz that youuu?ðððððð...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31958</th>\n",
       "      <td>31959</td>\n",
       "      <td>0</td>\n",
       "      <td>to see nina turner on the airwaves trying to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31959</th>\n",
       "      <td>31960</td>\n",
       "      <td>0</td>\n",
       "      <td>listening to sad songs on a monday morning otw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31960</th>\n",
       "      <td>31961</td>\n",
       "      <td>1</td>\n",
       "      <td>@user #sikh #temple vandalised in in #calgary,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31961</th>\n",
       "      <td>31962</td>\n",
       "      <td>0</td>\n",
       "      <td>thank you @user for you follow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31962 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  label                                              tweet\n",
       "0          1      0   @user when a father is dysfunctional and is s...\n",
       "1          2      0  @user @user thanks for #lyft credit i can't us...\n",
       "2          3      0                                bihday your majesty\n",
       "3          4      0  #model   i love u take with u all the time in ...\n",
       "4          5      0             factsguide: society now    #motivation\n",
       "...      ...    ...                                                ...\n",
       "31957  31958      0  ate @user isz that youuu?ðððððð...\n",
       "31958  31959      0    to see nina turner on the airwaves trying to...\n",
       "31959  31960      0  listening to sad songs on a monday morning otw...\n",
       "31960  31961      1  @user #sikh #temple vandalised in in #calgary,...\n",
       "31961  31962      0                   thank you @user for you follow  \n",
       "\n",
       "[31962 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df= tweets_df.drop(['id'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000017E71B69C48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets_df.hist(bins=30, figsize=(13, 5), color= 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17e74e0ce88>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(tweets_df['label'], label= 'Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df['length']= tweets_df['tweet'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive= tweets_df[tweets_df['label']==0]\n",
    "negative= tweets_df[tweets_df['label']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences= tweets_df['tweet'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_sentence= \" \".join(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17e75bbdfc8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "plt.imshow(WordCloud().generate(single_sentence))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Data\n",
    "Removing punctuations and stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to C:\\Users\\Siddhesh\n",
      "[nltk_data]     Menon\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import string\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def message_cleaning(message):\n",
    "    \n",
    "    #Removing punctuation from string passed based on string library\n",
    "    punctuation_removed= [char for char in message if char not in string.punctuation]\n",
    "    \n",
    "    #Joining the characters to form words\n",
    "    punctuation_removed_join= ''.join(punctuation_removed)\n",
    "    \n",
    "    #Removing Stopwords\n",
    "    punctuation_removed_join_clean= [word for word in punctuation_removed_join.split() if word.lower() not in stopwords.words('english')]\n",
    "    \n",
    "    return punctuation_removed_join_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [user, father, dysfunctional, selfish, drags, ...\n",
       "1        [user, user, thanks, lyft, credit, cant, use, ...\n",
       "2                                        [bihday, majesty]\n",
       "3        [model, love, u, take, u, time, urð±, ðð...\n",
       "4                        [factsguide, society, motivation]\n",
       "                               ...                        \n",
       "31957    [ate, user, isz, youuuðððððð...\n",
       "31958    [see, nina, turner, airwaves, trying, wrap, ma...\n",
       "31959    [listening, sad, songs, monday, morning, otw, ...\n",
       "31960    [user, sikh, temple, vandalised, calgary, wso,...\n",
       "31961                                [thank, user, follow]\n",
       "Name: tweet, Length: 31962, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying above cleaning function to the tweets\n",
    "tweets_cleaned= tweets_df['tweet'].apply(message_cleaning)\n",
    "tweets_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying Count Vectorization\n",
    "vectorizer= CountVectorizer(analyzer= message_cleaning)\n",
    "tweets_coutvectorizer= CountVectorizer(analyzer= message_cleaning, dtype='uint8').fit_transform(tweets_df['tweet']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= tweets_coutvectorizer\n",
    "y= tweets_df['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing a Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size= 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "NB_classifier= MultinomialNB()\n",
    "NB_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17e05190648>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predicting the Test set results\n",
    "y_predict_test= NB_classifier.predict(X_test)\n",
    "\n",
    "#Plotting confusion matrix to check for accuracy\n",
    "cm= confusion_matrix(y_test, y_predict_test)\n",
    "sb.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the Test Set:\n",
    "* The model has correctly classified 5800 tweets as positive and 2500 tweets as negative\n",
    "* It has incorrectly classified 160 tweets as positive and 210 tweets as negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.97      0.97      5933\n",
      "           1       0.61      0.54      0.57       460\n",
      "\n",
      "    accuracy                           0.94      6393\n",
      "   macro avg       0.79      0.76      0.77      6393\n",
      "weighted avg       0.94      0.94      0.94      6393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}