Sentiment Analysis_ VADER, TextBlob, ML Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis through VADER, TextBlob & Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_unit_id</th>\n",
       "      <th>_golden</th>\n",
       "      <th>_unit_state</th>\n",
       "      <th>_trusted_judgments</th>\n",
       "      <th>_last_judgment_at</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment:confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason:confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>681448150</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 5:24</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:35</td>\n",
       "      <td>5.703060e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>681448153</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 1:53</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>681448156</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 10:01</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>681448158</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 3:05</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>681448159</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 5:50</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:14</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    _unit_id  _golden _unit_state  _trusted_judgments _last_judgment_at  \\\n",
       "0  681448150    False   finalized                   3      2/25/15 5:24   \n",
       "1  681448153    False   finalized                   3      2/25/15 1:53   \n",
       "2  681448156    False   finalized                   3     2/25/15 10:01   \n",
       "3  681448158    False   finalized                   3      2/25/15 3:05   \n",
       "4  681448159    False   finalized                   3      2/25/15 5:50   \n",
       "\n",
       "  airline_sentiment  airline_sentiment:confidence negativereason  \\\n",
       "0           neutral                        1.0000            NaN   \n",
       "1          positive                        0.3486            NaN   \n",
       "2           neutral                        0.6837            NaN   \n",
       "3          negative                        1.0000     Bad Flight   \n",
       "4          negative                        1.0000     Can't Tell   \n",
       "\n",
       "   negativereason:confidence         airline airline_sentiment_gold  \\\n",
       "0                        NaN  Virgin America                    NaN   \n",
       "1                     0.0000  Virgin America                    NaN   \n",
       "2                        NaN  Virgin America                    NaN   \n",
       "3                     0.7033  Virgin America                    NaN   \n",
       "4                     1.0000  Virgin America                    NaN   \n",
       "\n",
       "         name negativereason_gold  retweet_count  \\\n",
       "0     cairdin                 NaN              0   \n",
       "1    jnardino                 NaN              0   \n",
       "2  yvonnalynn                 NaN              0   \n",
       "3    jnardino                 NaN              0   \n",
       "4    jnardino                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "\n",
       "   tweet_created      tweet_id tweet_location               user_timezone  \n",
       "0  2/24/15 11:35  5.703060e+17            NaN  Eastern Time (US & Canada)  \n",
       "1  2/24/15 11:15  5.703010e+17            NaN  Pacific Time (US & Canada)  \n",
       "2  2/24/15 11:15  5.703010e+17      Lets Play  Central Time (US & Canada)  \n",
       "3  2/24/15 11:15  5.703010e+17            NaN  Pacific Time (US & Canada)  \n",
       "4  2/24/15 11:14  5.703010e+17            NaN  Pacific Time (US & Canada)  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.read_csv('Airline-Sentiment-2-w-AA.csv',encoding='latin-1')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14640, 20)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from wordcloud import WordCloud\n",
    "from string import punctuation\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.probability import FreqDist\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import TweetTokenizer\n",
    "from autocorrect import Speller\n",
    "tweet_tok=  TweetTokenizer()\n",
    "spell=Speller(lang='en')\n",
    "stop_nltk= stopwords.words(\"english\")\n",
    "stop_updated= stop_nltk+['user']+['https://t.co.']+[' http://t.co/']+['...'] +['http co']\n",
    "lemm = WordNetLemmatizer()\n",
    "\n",
    "def clean_text(text):\n",
    "    tokens= tweet_tok.tokenize(text.lower())\n",
    "    stemmed=[lemm.lemmatize(term) for term in tokens if term not in  stop_updated and term not in list(punctuation) and len(term)>2]\n",
    "    stemmed_tok = [word for word in stemmed if word.isalpha()]\n",
    "    stemmed_tokens = [spell(word) for word in stemmed_tok]\n",
    "    res=' '.join(stemmed_tokens)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Clean_Tweets']= data['text'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Clean_Tweets']=data['Clean_Tweets'].str.replace('#','')\n",
    "data['Clean_Tweets']=data['Clean_Tweets'].str.replace('@','')\n",
    "data['Clean_Tweets']=data['Clean_Tweets'].str.replace('http','')\n",
    "data['Clean_Tweets']=data['Clean_Tweets'].str.replace('co','')\n",
    "data['Clean_Tweets']=data['Clean_Tweets'].str.replace('_ùª _ùª ','')\n",
    "data['Clean_Tweets']=data['Clean_Tweets'].str.replace('â_ù â_ù','')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis using VADER Lexicon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "analyser= SentimentIntensityAnalyzer() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vader_sentiment(sent):\n",
    "    return analyser.polarity_scores(sent)['compound']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['sent_vader']= data['Clean_Tweets'].apply(get_vader_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_pred=[]\n",
    "for i in range(len(data['sent_vader'])):\n",
    "    if data['sent_vader'][i]>=0.05:\n",
    "        sent_pred.append('positive')\n",
    "    elif data['sent_vader'][i]<=-0.05:\n",
    "        sent_pred.append('negative')\n",
    "    else:\n",
    "        sent_pred.append('neutral')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['sent_pred']= sent_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>_golden</th>\n",
       "      <th>_unit_state</th>\n",
       "      <th>_trusted_judgments</th>\n",
       "      <th>_last_judgment_at</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment:confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason:confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "      <th>Clean_Tweets</th>\n",
       "      <th>sent_vader</th>\n",
       "      <th>sent_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 5:24</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "      <td>said</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 1:53</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>plus added mmercial experience tacky</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 10:01</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "      <td>today must mean need take another trip</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 3:05</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>really aggressive blast obnoxious entertainmen...</td>\n",
       "      <td>-0.3306</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 5:50</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>really big bad thing</td>\n",
       "      <td>-0.5829</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _golden _unit_state  _trusted_judgments _last_judgment_at  \\\n",
       "0    False   finalized                   3      2/25/15 5:24   \n",
       "1    False   finalized                   3      2/25/15 1:53   \n",
       "2    False   finalized                   3     2/25/15 10:01   \n",
       "3    False   finalized                   3      2/25/15 3:05   \n",
       "4    False   finalized                   3      2/25/15 5:50   \n",
       "\n",
       "  airline_sentiment  airline_sentiment:confidence negativereason  \\\n",
       "0           neutral                        1.0000            NaN   \n",
       "1          positive                        0.3486            NaN   \n",
       "2           neutral                        0.6837            NaN   \n",
       "3          negative                        1.0000     Bad Flight   \n",
       "4          negative                        1.0000     Can't Tell   \n",
       "\n",
       "   negativereason:confidence         airline airline_sentiment_gold  \\\n",
       "0                        NaN  Virgin America                    NaN   \n",
       "1                     0.0000  Virgin America                    NaN   \n",
       "2                        NaN  Virgin America                    NaN   \n",
       "3                     0.7033  Virgin America                    NaN   \n",
       "4                     1.0000  Virgin America                    NaN   \n",
       "\n",
       "  negativereason_gold  retweet_count  \\\n",
       "0                 NaN              0   \n",
       "1                 NaN              0   \n",
       "2                 NaN              0   \n",
       "3                 NaN              0   \n",
       "4                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "\n",
       "   tweet_created tweet_location               user_timezone  \\\n",
       "0  2/24/15 11:35            NaN  Eastern Time (US & Canada)   \n",
       "1  2/24/15 11:15            NaN  Pacific Time (US & Canada)   \n",
       "2  2/24/15 11:15      Lets Play  Central Time (US & Canada)   \n",
       "3  2/24/15 11:15            NaN  Pacific Time (US & Canada)   \n",
       "4  2/24/15 11:14            NaN  Pacific Time (US & Canada)   \n",
       "\n",
       "                                        Clean_Tweets  sent_vader sent_pred  \n",
       "0                                               said      0.0000   neutral  \n",
       "1               plus added mmercial experience tacky      0.0000   neutral  \n",
       "2             today must mean need take another trip      0.0000   neutral  \n",
       "3  really aggressive blast obnoxious entertainmen...     -0.3306  negative  \n",
       "4                               really big bad thing     -0.5829  negative  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_golden</th>\n",
       "      <th>_unit_state</th>\n",
       "      <th>_trusted_judgments</th>\n",
       "      <th>_last_judgment_at</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment:confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason:confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>...</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "      <th>Clean_Tweets</th>\n",
       "      <th>sent_vader</th>\n",
       "      <th>sent_pred</th>\n",
       "      <th>flagged_sentiment</th>\n",
       "      <th>predicted_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 5:24</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "      <td>said</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 1:53</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>plus added mmercial experience tacky</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 10:01</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "      <td>today must mean need take another trip</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 3:05</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>really aggressive blast obnoxious entertainmen...</td>\n",
       "      <td>-0.3306</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 5:50</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>really big bad thing</td>\n",
       "      <td>-0.5829</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   _golden _unit_state  _trusted_judgments _last_judgment_at  \\\n",
       "0    False   finalized                   3      2/25/15 5:24   \n",
       "1    False   finalized                   3      2/25/15 1:53   \n",
       "2    False   finalized                   3     2/25/15 10:01   \n",
       "3    False   finalized                   3      2/25/15 3:05   \n",
       "4    False   finalized                   3      2/25/15 5:50   \n",
       "\n",
       "  airline_sentiment  airline_sentiment:confidence negativereason  \\\n",
       "0           neutral                        1.0000            NaN   \n",
       "1          positive                        0.3486            NaN   \n",
       "2           neutral                        0.6837            NaN   \n",
       "3          negative                        1.0000     Bad Flight   \n",
       "4          negative                        1.0000     Can't Tell   \n",
       "\n",
       "   negativereason:confidence         airline airline_sentiment_gold  ...  \\\n",
       "0                        NaN  Virgin America                    NaN  ...   \n",
       "1                     0.0000  Virgin America                    NaN  ...   \n",
       "2                        NaN  Virgin America                    NaN  ...   \n",
       "3                     0.7033  Virgin America                    NaN  ...   \n",
       "4                     1.0000  Virgin America                    NaN  ...   \n",
       "\n",
       "                                                text  tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.          NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...          NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...          NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...          NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...          NaN   \n",
       "\n",
       "   tweet_created tweet_location               user_timezone  \\\n",
       "0  2/24/15 11:35            NaN  Eastern Time (US & Canada)   \n",
       "1  2/24/15 11:15            NaN  Pacific Time (US & Canada)   \n",
       "2  2/24/15 11:15      Lets Play  Central Time (US & Canada)   \n",
       "3  2/24/15 11:15            NaN  Pacific Time (US & Canada)   \n",
       "4  2/24/15 11:14            NaN  Pacific Time (US & Canada)   \n",
       "\n",
       "                                        Clean_Tweets sent_vader sent_pred  \\\n",
       "0                                               said     0.0000   neutral   \n",
       "1               plus added mmercial experience tacky     0.0000   neutral   \n",
       "2             today must mean need take another trip     0.0000   neutral   \n",
       "3  really aggressive blast obnoxious entertainmen...    -0.3306  negative   \n",
       "4                               really big bad thing    -0.5829  negative   \n",
       "\n",
       "   flagged_sentiment predicted_sentiment  \n",
       "0                  1                   1  \n",
       "1                  2                   1  \n",
       "2                  1                   1  \n",
       "3                  0                   0  \n",
       "4                  0                   0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "x1=LabelEncoder()  #Creating a label-encoder obj\n",
    "data['flagged_sentiment']= x1.fit_transform(data['airline_sentiment'])\n",
    "data['predicted_sentiment']= x1.fit_transform(data['sent_pred']) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4956967213114754"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "accuracy_score(data['flagged_sentiment'],data['predicted_sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3874, 2230, 3074],\n",
       "       [ 406, 1393, 1300],\n",
       "       [  89,  284, 1990]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(data['flagged_sentiment'],data['predicted_sentiment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy of VADER lexicon is 49.56% "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis with TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "def get_textblob_sentiment(sent):\n",
    "    return TextBlob(sent).sentiment.polarity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['sent_TextBlob']= data['Clean_Tweets'].apply(get_textblob_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_pred=[]\n",
    "for i in range(len(data['sent_TextBlob'])):\n",
    "    if data['sent_TextBlob'][i]>=0.05:\n",
    "        sent_pred.append('positive')\n",
    "    elif data['sent_TextBlob'][i]<=-0.05:\n",
    "        sent_pred.append('negative')\n",
    "    else:\n",
    "        sent_pred.append('neutral')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['sent_pred']= sent_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_unit_id</th>\n",
       "      <th>_golden</th>\n",
       "      <th>_unit_state</th>\n",
       "      <th>_trusted_judgments</th>\n",
       "      <th>_last_judgment_at</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment:confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason:confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>...</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "      <th>Clean_Tweets</th>\n",
       "      <th>sent_TextBlob</th>\n",
       "      <th>sent_pred</th>\n",
       "      <th>flagged_sentiment</th>\n",
       "      <th>predicted_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>681448150</td>\n",
       "      <td>False</td>\n",
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       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:35</td>\n",
       "      <td>5.703060e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "      <td>said</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>681448153</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 1:53</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>plus added mmercial experience tacky</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>681448156</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 10:01</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "      <td>today must mean need take another trip</td>\n",
       "      <td>-0.31250</td>\n",
       "      <td>negative</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>681448158</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 3:05</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:15</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>really aggressive blast obnoxious entertainmen...</td>\n",
       "      <td>0.00625</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>681448159</td>\n",
       "      <td>False</td>\n",
       "      <td>finalized</td>\n",
       "      <td>3</td>\n",
       "      <td>2/25/15 5:50</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/24/15 11:14</td>\n",
       "      <td>5.703010e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "      <td>really big bad thing</td>\n",
       "      <td>-0.35000</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    _unit_id  _golden _unit_state  _trusted_judgments _last_judgment_at  \\\n",
       "0  681448150    False   finalized                   3      2/25/15 5:24   \n",
       "1  681448153    False   finalized                   3      2/25/15 1:53   \n",
       "2  681448156    False   finalized                   3     2/25/15 10:01   \n",
       "3  681448158    False   finalized                   3      2/25/15 3:05   \n",
       "4  681448159    False   finalized                   3      2/25/15 5:50   \n",
       "\n",
       "  airline_sentiment  airline_sentiment:confidence negativereason  \\\n",
       "0           neutral                        1.0000            NaN   \n",
       "1          positive                        0.3486            NaN   \n",
       "2           neutral                        0.6837            NaN   \n",
       "3          negative                        1.0000     Bad Flight   \n",
       "4          negative                        1.0000     Can't Tell   \n",
       "\n",
       "   negativereason:confidence         airline  ... tweet_coord  tweet_created  \\\n",
       "0                        NaN  Virgin America  ...         NaN  2/24/15 11:35   \n",
       "1                     0.0000  Virgin America  ...         NaN  2/24/15 11:15   \n",
       "2                        NaN  Virgin America  ...         NaN  2/24/15 11:15   \n",
       "3                     0.7033  Virgin America  ...         NaN  2/24/15 11:15   \n",
       "4                     1.0000  Virgin America  ...         NaN  2/24/15 11:14   \n",
       "\n",
       "       tweet_id  tweet_location               user_timezone  \\\n",
       "0  5.703060e+17             NaN  Eastern Time (US & Canada)   \n",
       "1  5.703010e+17             NaN  Pacific Time (US & Canada)   \n",
       "2  5.703010e+17       Lets Play  Central Time (US & Canada)   \n",
       "3  5.703010e+17             NaN  Pacific Time (US & Canada)   \n",
       "4  5.703010e+17             NaN  Pacific Time (US & Canada)   \n",
       "\n",
       "                                        Clean_Tweets sent_TextBlob  sent_pred  \\\n",
       "0                                               said       0.00000    neutral   \n",
       "1               plus added mmercial experience tacky       0.00000    neutral   \n",
       "2             today must mean need take another trip      -0.31250   negative   \n",
       "3  really aggressive blast obnoxious entertainmen...       0.00625    neutral   \n",
       "4                               really big bad thing      -0.35000   negative   \n",
       "\n",
       "  flagged_sentiment predicted_sentiment  \n",
       "0                 1                   1  \n",
       "1                 2                   1  \n",
       "2                 1                   0  \n",
       "3                 0                   1  \n",
       "4                 0                   0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "x1=LabelEncoder()  #Creating a label-encoder obj\n",
    "data['flagged_sentiment']= x1.fit_transform(data['airline_sentiment'])\n",
    "data['predicted_sentiment']= x1.fit_transform(data['sent_pred']) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42916666666666664"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "accuracy_score(data['flagged_sentiment'],data['predicted_sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2666, 4042, 2470],\n",
       "       [ 249, 1922,  928],\n",
       "       [  84,  584, 1695]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(data['flagged_sentiment'],data['predicted_sentiment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy using TextBlob is 42.91%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis with using Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= data['Clean_Tweets'].values\n",
    "y= data['flagged_sentiment'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vect= CountVectorizer()\n",
    "x_train_bow= count_vect.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_bow= count_vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10248, 6791) (4392, 6791)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_bow.shape,x_test_bow.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arpitha Ananth\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Arpitha Ananth\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LogReg= LogisticRegression()\n",
    "LogReg= LogReg.fit(x_train_bow,y_train)\n",
    "\n",
    "y_pred= LogReg.predict(x_test_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7873406193078324\n",
      "Confusion Matrix: \n",
      "[[2535  200   79]\n",
      " [ 348  462   74]\n",
      " [ 143   90  461]]\n"
     ]
    }
   ],
   "source": [
    "LRA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \", LRA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.775424530222006"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "LogReg= multiclass_roc_auc_score(y_test,y_pred)\n",
    "LogReg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import  MultinomialNB\n",
    "\n",
    "mnb= MultinomialNB()\n",
    "mnb=mnb.fit(x_train_bow.toarray(),y_train)\n",
    "\n",
    "y_pred= mnb.predict(x_test_bow.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.76775956284153\n",
      "Confusion Matrix: \n",
      "[[2624  128   62]\n",
      " [ 496  327   61]\n",
      " [ 203   70  421]]\n"
     ]
    }
   ],
   "source": [
    "NBA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \",NBA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.729391463354013"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "NB= multiclass_roc_auc_score(y_test,y_pred)\n",
    "NB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn= KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(x_train_bow.toarray(),y_train)\n",
    "\n",
    "y_pred= knn.predict(x_test_bow.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.5311930783242259\n",
      "Confusion Matrix: \n",
      "[[1297 1389  128]\n",
      " [ 154  669   61]\n",
      " [  83  244  367]]\n"
     ]
    }
   ],
   "source": [
    "KNNA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \",KNNA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6799515463402511"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "KNN= multiclass_roc_auc_score(y_test,y_pred)\n",
    "KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc= DecisionTreeClassifier(criterion=\"entropy\")\n",
    "dtc.fit(x_train_bow.toarray(),y_train)\n",
    "y_pred= dtc.predict(x_test_bow.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.6910291438979964\n",
      "Confusion Matrix: \n",
      "[[2170  459  185]\n",
      " [ 300  461  123]\n",
      " [ 160  130  404]]\n"
     ]
    }
   ],
   "source": [
    "DTA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \",DTA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7220119359148245"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "DT= multiclass_roc_auc_score(y_test,y_pred)\n",
    "DT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf= RandomForestClassifier(n_estimators=100,random_state=0)\n",
    "rf.fit(x_train_bow.toarray(),y_train)\n",
    "y_pred= rf.predict(x_test_bow.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7609289617486339\n",
      "Confusion Matrix: \n",
      "[[2476  240   98]\n",
      " [ 362  429   93]\n",
      " [ 178   79  437]]\n"
     ]
    }
   ],
   "source": [
    "RFA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \",RFA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7516789214082253"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "RF= multiclass_roc_auc_score(y_test,y_pred)\n",
    "RF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ada Boost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada= AdaBoostClassifier(n_estimators=100,random_state=0)\n",
    "ada.fit(x_train_bow.toarray(),y_train)\n",
    "y_pred= ada.predict(x_test_bow.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7165300546448088\n",
      "Confusion Matrix: \n",
      "[[2303  410  101]\n",
      " [ 371  434   79]\n",
      " [ 184  100  410]]\n"
     ]
    }
   ],
   "source": [
    "ADAA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \",ADAA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7257280523893445"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "AD= multiclass_roc_auc_score(y_test,y_pred)\n",
    "AD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boost Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient= GradientBoostingClassifier(n_estimators=200,random_state=0,max_depth=2)\n",
    "gradient.fit(x_train_bow.toarray(),y_train)\n",
    "y_pred= gradient.predict(x_test_bow.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7199453551912568\n",
      "Confusion Matrix: \n",
      "[[2698   30   86]\n",
      " [ 739   81   64]\n",
      " [ 298   13  383]]\n"
     ]
    }
   ],
   "source": [
    "GBA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \",GBA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6487164288090904"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "GB= multiclass_roc_auc_score(y_test,y_pred)\n",
    "GB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XG Boost Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "xgb_model = xgb.XGBClassifier()\n",
    "Xg= GradientBoostingClassifier(n_estimators=200,random_state=0,max_depth=2)\n",
    "Xg.fit(x_train_bow.toarray(),y_train)\n",
    "y_pred= Xg.predict(x_test_bow.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7199453551912568\n",
      "Confusion Matrix: \n",
      "[[2698   30   86]\n",
      " [ 739   81   64]\n",
      " [ 298   13  383]]\n"
     ]
    }
   ],
   "source": [
    "XGA= metrics.accuracy_score(y_pred,y_test)\n",
    "print(\"Accuracy Score: \",XGA)\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(\"Confusion Matrix: \",cm,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6487164288090904"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "XG= multiclass_roc_auc_score(y_test,y_pred)\n",
    "XG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison Of ML Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Accuracy</th>\n",
       "      <th>AUC-ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.787341</td>\n",
       "      <td>0.775425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.767760</td>\n",
       "      <td>0.729391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.531193</td>\n",
       "      <td>0.679952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.691029</td>\n",
       "      <td>0.722012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.760929</td>\n",
       "      <td>0.751679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ada Boost</th>\n",
       "      <td>0.716530</td>\n",
       "      <td>0.725728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boost</th>\n",
       "      <td>0.719945</td>\n",
       "      <td>0.648716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XG Boost</th>\n",
       "      <td>0.719945</td>\n",
       "      <td>0.648716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy   AUC-ROC\n",
       "Logistic Regression  0.787341  0.775425\n",
       "Naive Bayes          0.767760  0.729391\n",
       "KNN                  0.531193  0.679952\n",
       "Decision Tree        0.691029  0.722012\n",
       "Random Forest        0.760929  0.751679\n",
       "Ada Boost            0.716530  0.725728\n",
       "Gradient Boost       0.719945  0.648716\n",
       "XG Boost             0.719945  0.648716"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Algorithms= ['Logistic Regression','Naive Bayes','KNN','Decision Tree','Random Forest','Ada Boost','Gradient Boost','XG Boost']\n",
    "Accuracy= [LRA,NBA,KNNA,DTA,RFA,ADAA,GBA,XGA]\n",
    "AUC_ROC= [LogReg,NB,KNN,DT,RF,AD,GB,XG]\n",
    "comp= pd.DataFrame([Accuracy,AUC_ROC]).T\n",
    "comp.set_index([Algorithms],inplace=True)\n",
    "comp.columns=['Accuracy','AUC-ROC']\n",
    "comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x242b17f5860>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp.plot(kind='bar',figsize=(15,5),rot=0,title='Model Comparison',color=['darkkhaki','crimson'])"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}