lexicon_based_sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rule based Sentiment Analysis\n",
    "using TextBlob, VADER, SentiWordNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preprocessing steps\n",
    "1. Cleaning the text\n",
    "2. Tokenization\n",
    "3. Enrichment - POS tagging\n",
    "4. Stopwords removal\n",
    "5. Obtaining the stem words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Unnamed: 0</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                             review\n",
       "0           0  I called because my food was cold and not done...\n",
       "1           1  OMG, hands down the best pizza I've had from D...\n",
       "2           2  This Domino's has the best pizza delivery and ...\n",
       "3           3  My Sweetheart & I are very pleased with the qu...\n",
       "4           4  I called to place an order, The lady answered ..."
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating a pandas dataframe from reviews.txt file\n",
    "data = pd.read_csv('reviews.txt', sep='\\t')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping unwanted columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review\n",
       "0  I called because my food was cold and not done...\n",
       "1  OMG, hands down the best pizza I've had from D...\n",
       "2  This Domino's has the best pizza delivery and ...\n",
       "3  My Sweetheart & I are very pleased with the qu...\n",
       "4  I called to place an order, The lady answered ..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping Unnamed: 0 column\n",
    "mydata = data.drop('Unnamed: 0', axis=1)\n",
    "mydata.head()\n",
    "\n",
    "# mydata = pd.DataFrame(data.review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Cleaning the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>review</th>\n",
       "      <th>Cleaned Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "      <td>OMG hands down the best pizza I ve had from Do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "      <td>This Domino s has the best pizza delivery and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "      <td>My Sweetheart I are very pleased with the qual...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "      <td>I called to place an order The lady answered a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG, hands down the best pizza I've had from D...   \n",
       "2  This Domino's has the best pizza delivery and ...   \n",
       "3  My Sweetheart & I are very pleased with the qu...   \n",
       "4  I called to place an order, The lady answered ...   \n",
       "\n",
       "                                     Cleaned Reviews  \n",
       "0  I called because my food was cold and not done...  \n",
       "1  OMG hands down the best pizza I ve had from Do...  \n",
       "2  This Domino s has the best pizza delivery and ...  \n",
       "3  My Sweetheart I are very pleased with the qual...  \n",
       "4  I called to place an order The lady answered a...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Define a function to clean the text\n",
    "def clean(text):\n",
    "    # Removes all special characters and numericals leaving the alphabets\n",
    "    text = re.sub('[^A-Za-z]+', ' ', text) \n",
    "    return text\n",
    "\n",
    "# Cleaning the text in the review column\n",
    "mydata['Cleaned Reviews'] = mydata['review'].apply(clean)\n",
    "mydata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps 2-4: Tokenization, POS tagging, stopwords removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\harika\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\harika\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\harika\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "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>review</th>\n",
       "      <th>Cleaned Reviews</th>\n",
       "      <th>POS tagged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>[(called, v), (food, n), (cold, a), (done, v),...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "      <td>OMG hands down the best pizza I ve had from Do...</td>\n",
       "      <td>[(OMG, n), (hands, v), (best, a), (pizza, n), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "      <td>This Domino s has the best pizza delivery and ...</td>\n",
       "      <td>[(Domino, n), (best, a), (pizza, n), (delivery...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "      <td>My Sweetheart I are very pleased with the qual...</td>\n",
       "      <td>[(Sweetheart, n), (pleased, a), (quality, n), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "      <td>I called to place an order The lady answered a...</td>\n",
       "      <td>[(called, v), (place, v), (order, n), (lady, n...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG, hands down the best pizza I've had from D...   \n",
       "2  This Domino's has the best pizza delivery and ...   \n",
       "3  My Sweetheart & I are very pleased with the qu...   \n",
       "4  I called to place an order, The lady answered ...   \n",
       "\n",
       "                                     Cleaned Reviews  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG hands down the best pizza I ve had from Do...   \n",
       "2  This Domino s has the best pizza delivery and ...   \n",
       "3  My Sweetheart I are very pleased with the qual...   \n",
       "4  I called to place an order The lady answered a...   \n",
       "\n",
       "                                          POS tagged  \n",
       "0  [(called, v), (food, n), (cold, a), (done, v),...  \n",
       "1  [(OMG, n), (hands, v), (best, a), (pizza, n), ...  \n",
       "2  [(Domino, n), (best, a), (pizza, n), (delivery...  \n",
       "3  [(Sweetheart, n), (pleased, a), (quality, n), ...  \n",
       "4  [(called, v), (place, v), (order, n), (lady, n...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk import pos_tag\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('wordnet')\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "# POS tagger dictionary\n",
    "pos_dict = {'J':wordnet.ADJ, 'V':wordnet.VERB, 'N':wordnet.NOUN, 'R':wordnet.ADV}\n",
    "\n",
    "def token_stop_pos(text):\n",
    "    tags = pos_tag(word_tokenize(text))\n",
    "    newlist = []\n",
    "    for word, tag in tags:\n",
    "        if word.lower() not in set(stopwords.words('english')):\n",
    "            newlist.append(tuple([word, pos_dict.get(tag[0])]))\n",
    "    return newlist\n",
    "\n",
    "mydata['POS tagged'] = mydata['Cleaned Reviews'].apply(token_stop_pos)\n",
    "mydata.head()\n",
    "# mydata['POS tagged'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Obtaining the stem words\n",
    "\n",
    "#### Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>review</th>\n",
       "      <th>Cleaned Reviews</th>\n",
       "      <th>POS tagged</th>\n",
       "      <th>Lemma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>[(called, v), (food, n), (cold, a), (done, v),...</td>\n",
       "      <td>call food cold do right miss item call answe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "      <td>OMG hands down the best pizza I ve had from Do...</td>\n",
       "      <td>[(OMG, n), (hands, v), (best, a), (pizza, n), ...</td>\n",
       "      <td>OMG hand best pizza Domino pizza Southaven M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "      <td>This Domino s has the best pizza delivery and ...</td>\n",
       "      <td>[(Domino, n), (best, a), (pizza, n), (delivery...</td>\n",
       "      <td>Domino best pizza delivery customer service ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "      <td>My Sweetheart I are very pleased with the qual...</td>\n",
       "      <td>[(Sweetheart, n), (pleased, a), (quality, n), ...</td>\n",
       "      <td>Sweetheart pleased quality service excellent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "      <td>I called to place an order The lady answered a...</td>\n",
       "      <td>[(called, v), (place, v), (order, n), (lady, n...</td>\n",
       "      <td>call place order lady answer already know na...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG, hands down the best pizza I've had from D...   \n",
       "2  This Domino's has the best pizza delivery and ...   \n",
       "3  My Sweetheart & I are very pleased with the qu...   \n",
       "4  I called to place an order, The lady answered ...   \n",
       "\n",
       "                                     Cleaned Reviews  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG hands down the best pizza I ve had from Do...   \n",
       "2  This Domino s has the best pizza delivery and ...   \n",
       "3  My Sweetheart I are very pleased with the qual...   \n",
       "4  I called to place an order The lady answered a...   \n",
       "\n",
       "                                          POS tagged  \\\n",
       "0  [(called, v), (food, n), (cold, a), (done, v),...   \n",
       "1  [(OMG, n), (hands, v), (best, a), (pizza, n), ...   \n",
       "2  [(Domino, n), (best, a), (pizza, n), (delivery...   \n",
       "3  [(Sweetheart, n), (pleased, a), (quality, n), ...   \n",
       "4  [(called, v), (place, v), (order, n), (lady, n...   \n",
       "\n",
       "                                               Lemma  \n",
       "0    call food cold do right miss item call answe...  \n",
       "1    OMG hand best pizza Domino pizza Southaven M...  \n",
       "2    Domino best pizza delivery customer service ...  \n",
       "3    Sweetheart pleased quality service excellent...  \n",
       "4    call place order lady answer already know na...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def lemmatize(pos_data):\n",
    "    lemma_rew = \" \"\n",
    "    for word, pos in pos_data:\n",
    "        if not pos: \n",
    "            lemma = word\n",
    "            lemma_rew = lemma_rew + \" \" + lemma\n",
    "        else:  \n",
    "            lemma = wordnet_lemmatizer.lemmatize(word, pos=pos)\n",
    "            lemma_rew = lemma_rew + \" \" + lemma\n",
    "    return lemma_rew\n",
    "    \n",
    "mydata['Lemma'] = mydata['POS tagged'].apply(lemmatize)\n",
    "mydata.head()\n",
    "# print(mydata['review'][239] + \"\\n\" + mydata['Lemma'][239])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>review</th>\n",
       "      <th>Lemma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>call food cold do right miss item call answe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "      <td>OMG hand best pizza Domino pizza Southaven M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "      <td>Domino best pizza delivery customer service ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "      <td>Sweetheart pleased quality service excellent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "      <td>call place order lady answer already know na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Good quality and cheap pizzas. They offer wide...</td>\n",
       "      <td>Good quality cheap pizza offer wide range pi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Every time I order online they forget to put m...</td>\n",
       "      <td>Every time order online forget put olive ord...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Store: SHOP NO. 17 &amp; 18, A WING, HALLMARK, VAS...</td>\n",
       "      <td>Store SHOP WING HALLMARK VASANT OSCAR LBS MA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Chicken topping was so good, I could not sit s...</td>\n",
       "      <td>Chicken topping good could sit still excelle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>People reading this don't place an order in Do...</td>\n",
       "      <td>People read place order Domino still want pl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                review  \\\n",
       "0    I called because my food was cold and not done...   \n",
       "1    OMG, hands down the best pizza I've had from D...   \n",
       "2    This Domino's has the best pizza delivery and ...   \n",
       "3    My Sweetheart & I are very pleased with the qu...   \n",
       "4    I called to place an order, The lady answered ...   \n",
       "..                                                 ...   \n",
       "235  Good quality and cheap pizzas. They offer wide...   \n",
       "236  Every time I order online they forget to put m...   \n",
       "237  Store: SHOP NO. 17 & 18, A WING, HALLMARK, VAS...   \n",
       "238  Chicken topping was so good, I could not sit s...   \n",
       "239  People reading this don't place an order in Do...   \n",
       "\n",
       "                                                 Lemma  \n",
       "0      call food cold do right miss item call answe...  \n",
       "1      OMG hand best pizza Domino pizza Southaven M...  \n",
       "2      Domino best pizza delivery customer service ...  \n",
       "3      Sweetheart pleased quality service excellent...  \n",
       "4      call place order lady answer already know na...  \n",
       "..                                                 ...  \n",
       "235    Good quality cheap pizza offer wide range pi...  \n",
       "236    Every time order online forget put olive ord...  \n",
       "237    Store SHOP WING HALLMARK VASANT OSCAR LBS MA...  \n",
       "238    Chicken topping good could sit still excelle...  \n",
       "239    People read place order Domino still want pl...  \n",
       "\n",
       "[240 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[['review', 'Lemma']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment analysis using TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "\n",
    "# function to calculate subjectivity \n",
    "def getSubjectivity(review):\n",
    "    return TextBlob(review).sentiment.subjectivity\n",
    "\n",
    "# function to calculate polarity\n",
    "def getPolarity(review):\n",
    "    return TextBlob(review).sentiment.polarity\n",
    "\n",
    "# function to analyze the reviews\n",
    "def analysis(score):\n",
    "    if score < 0:\n",
    "        return 'Negative'\n",
    "    elif score == 0:\n",
    "        return 'Neutral'\n",
    "    else:\n",
    "        return 'Positive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin_data = pd.DataFrame(mydata[['review', 'Lemma']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>review</th>\n",
       "      <th>Lemma</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>call food cold do right miss item call answe...</td>\n",
       "      <td>0.217143</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "      <td>OMG hand best pizza Domino pizza Southaven M...</td>\n",
       "      <td>0.538889</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "      <td>Domino best pizza delivery customer service ...</td>\n",
       "      <td>0.355000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "      <td>Sweetheart pleased quality service excellent...</td>\n",
       "      <td>0.238542</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "      <td>call place order lady answer already know na...</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG, hands down the best pizza I've had from D...   \n",
       "2  This Domino's has the best pizza delivery and ...   \n",
       "3  My Sweetheart & I are very pleased with the qu...   \n",
       "4  I called to place an order, The lady answered ...   \n",
       "\n",
       "                                               Lemma  Polarity  Analysis  \n",
       "0    call food cold do right miss item call answe...  0.217143  Positive  \n",
       "1    OMG hand best pizza Domino pizza Southaven M...  0.538889  Positive  \n",
       "2    Domino best pizza delivery customer service ...  0.355000  Positive  \n",
       "3    Sweetheart pleased quality service excellent...  0.238542  Positive  \n",
       "4    call place order lady answer already know na...  0.650000  Positive  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fin_data['Subjectivity'] = fin_data['Lemma'].apply(getSubjectivity) \n",
    "fin_data['Polarity'] = fin_data['Lemma'].apply(getPolarity) \n",
    "fin_data['Analysis'] = fin_data['Polarity'].apply(analysis)\n",
    "fin_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Negative    119\n",
       "Positive    114\n",
       "Neutral       7\n",
       "Name: Analysis, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_counts = fin_data.Analysis.value_counts()\n",
    "tb_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x183c71c6388>,\n",
       "  <matplotlib.patches.Wedge at 0x183c718e948>,\n",
       "  <matplotlib.patches.Wedge at 0x183c71ca148>],\n",
       " [Text(0.014398541397730972, 1.099905760511153, 'Negative'),\n",
       "  Text(-0.11498130276606976, -1.093974085622789, 'Positive'),\n",
       "  Text(1.3443366507319228, -0.12352720145327041, 'Neutral')],\n",
       " [Text(0.007853749853307801, 0.599948596642447, '49.6%'),\n",
       "  Text(-0.06271707423603803, -0.5967131376124304, '47.5%'),\n",
       "  Text(0.8464341874978771, -0.07777638610020729, '2.9%')])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "tb_count= fin_data.Analysis.value_counts()\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.pie(tb_counts.values, labels = tb_counts.index, explode = (0, 0, 0.25), autopct='%1.1f%%', shadow=False)\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment analysis using VADER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# function to calculate vader sentiment  \n",
    "def vadersentimentanalysis(review):\n",
    "    vs = analyzer.polarity_scores(review)\n",
    "    return vs['compound']\n",
    "\n",
    "fin_data['Vader Sentiment'] = fin_data['Lemma'].apply(vadersentimentanalysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>review</th>\n",
       "      <th>Lemma</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Analysis</th>\n",
       "      <th>Vader Sentiment</th>\n",
       "      <th>Vader Analysis</th>\n",
       "      <th>SWN analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>call food cold do right miss item call answe...</td>\n",
       "      <td>0.217143</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.7579</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "      <td>OMG hand best pizza Domino pizza Southaven M...</td>\n",
       "      <td>0.538889</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.9312</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "      <td>Domino best pizza delivery customer service ...</td>\n",
       "      <td>0.355000</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.9552</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "      <td>Sweetheart pleased quality service excellent...</td>\n",
       "      <td>0.238542</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.9716</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "      <td>call place order lady answer already know na...</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.8091</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG, hands down the best pizza I've had from D...   \n",
       "2  This Domino's has the best pizza delivery and ...   \n",
       "3  My Sweetheart & I are very pleased with the qu...   \n",
       "4  I called to place an order, The lady answered ...   \n",
       "\n",
       "                                               Lemma  Polarity  Analysis  \\\n",
       "0    call food cold do right miss item call answe...  0.217143  Positive   \n",
       "1    OMG hand best pizza Domino pizza Southaven M...  0.538889  Positive   \n",
       "2    Domino best pizza delivery customer service ...  0.355000  Positive   \n",
       "3    Sweetheart pleased quality service excellent...  0.238542  Positive   \n",
       "4    call place order lady answer already know na...  0.650000  Positive   \n",
       "\n",
       "   Vader Sentiment Vader Analysis SWN analysis  \n",
       "0           0.7579       Positive     Positive  \n",
       "1           0.9312       Positive     Positive  \n",
       "2           0.9552       Positive     Positive  \n",
       "3           0.9716       Positive     Positive  \n",
       "4           0.8091       Positive     Positive  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to analyse \n",
    "def vader_analysis(compound):\n",
    "    if compound >= 0.5:\n",
    "        return 'Positive'\n",
    "    elif compound <= -0.5 :\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "    \n",
    "fin_data['Vader Analysis'] = fin_data['Vader Sentiment'].apply(vader_analysis)\n",
    "fin_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neutral     93\n",
       "Positive    82\n",
       "Negative    65\n",
       "Name: Vader Analysis, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vader_counts = fin_data['Vader Analysis'].value_counts()\n",
    "vader_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x183bcef8d88>,\n",
       "  <matplotlib.patches.Wedge at 0x183bcefaec8>,\n",
       "  <matplotlib.patches.Wedge at 0x183bcefca08>],\n",
       " [Text(0.41534051065610117, 1.1258295875521878, 'Neutral'),\n",
       "  Text(-1.0269384962128683, -0.3942046739906225, 'Positive'),\n",
       "  Text(0.7252803707995565, -0.8270238108620924, 'Negative')],\n",
       " [Text(0.24228196454939235, 0.6567339260721095, '38.7%'),\n",
       "  Text(-0.5601482706615645, -0.21502073126761223, '34.2%'),\n",
       "  Text(0.3956074749815762, -0.45110389683386853, '27.1%')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vader_counts= fin_data['Vader Analysis'].value_counts()\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.pie(vader_counts.values, labels = vader_counts.index, explode = (0.1, 0, 0), autopct='%1.1f%%', shadow=False)\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis using SentiWordNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package sentiwordnet to\n",
      "[nltk_data]     C:\\Users\\harika\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package sentiwordnet is already up-to-date!\n"
     ]
    },
    {
     "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>review</th>\n",
       "      <th>Lemma</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Analysis</th>\n",
       "      <th>Vader Sentiment</th>\n",
       "      <th>Vader Analysis</th>\n",
       "      <th>SWN analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I called because my food was cold and not done...</td>\n",
       "      <td>call food cold do right miss item call answe...</td>\n",
       "      <td>0.217143</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.7579</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMG, hands down the best pizza I've had from D...</td>\n",
       "      <td>OMG hand best pizza Domino pizza Southaven M...</td>\n",
       "      <td>0.538889</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.9312</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This Domino's has the best pizza delivery and ...</td>\n",
       "      <td>Domino best pizza delivery customer service ...</td>\n",
       "      <td>0.355000</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.9552</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>My Sweetheart &amp; I are very pleased with the qu...</td>\n",
       "      <td>Sweetheart pleased quality service excellent...</td>\n",
       "      <td>0.238542</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.9716</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I called to place an order, The lady answered ...</td>\n",
       "      <td>call place order lady answer already know na...</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.8091</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  \\\n",
       "0  I called because my food was cold and not done...   \n",
       "1  OMG, hands down the best pizza I've had from D...   \n",
       "2  This Domino's has the best pizza delivery and ...   \n",
       "3  My Sweetheart & I are very pleased with the qu...   \n",
       "4  I called to place an order, The lady answered ...   \n",
       "\n",
       "                                               Lemma  Polarity  Analysis  \\\n",
       "0    call food cold do right miss item call answe...  0.217143  Positive   \n",
       "1    OMG hand best pizza Domino pizza Southaven M...  0.538889  Positive   \n",
       "2    Domino best pizza delivery customer service ...  0.355000  Positive   \n",
       "3    Sweetheart pleased quality service excellent...  0.238542  Positive   \n",
       "4    call place order lady answer already know na...  0.650000  Positive   \n",
       "\n",
       "   Vader Sentiment Vader Analysis SWN analysis  \n",
       "0           0.7579       Positive     Positive  \n",
       "1           0.9312       Positive     Positive  \n",
       "2           0.9552       Positive     Positive  \n",
       "3           0.9716       Positive     Positive  \n",
       "4           0.8091       Positive     Positive  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('sentiwordnet')\n",
    "from nltk.corpus import sentiwordnet as swn\n",
    "\n",
    "def sentiwordnetanalysis(pos_data):\n",
    "    sentiment = 0\n",
    "    tokens_count = 0\n",
    "    for word, pos in pos_data:\n",
    "        if not pos:\n",
    "            continue\n",
    "        lemma = wordnet_lemmatizer.lemmatize(word, pos=pos)\n",
    "        if not lemma:\n",
    "            continue\n",
    "        \n",
    "        synsets = wordnet.synsets(lemma, pos=pos)\n",
    "        if not synsets:\n",
    "            continue\n",
    "\n",
    "        # Take the first sense, the most common\n",
    "        synset = synsets[0]\n",
    "        swn_synset = swn.senti_synset(synset.name())\n",
    "        sentiment += swn_synset.pos_score() - swn_synset.neg_score()\n",
    "        tokens_count += 1\n",
    "        # print(swn_synset.pos_score(),swn_synset.neg_score(),swn_synset.obj_score())\n",
    "    if not tokens_count:\n",
    "        return 0\n",
    "    if sentiment>0:\n",
    "        return \"Positive\"\n",
    "    if sentiment==0:\n",
    "        return \"Neutral\"\n",
    "    else:\n",
    "        return \"Negative\"\n",
    "\n",
    "fin_data['SWN analysis'] = mydata['POS tagged'].apply(sentiwordnetanalysis)\n",
    "fin_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Negative    135\n",
       "Positive    100\n",
       "Neutral       5\n",
       "Name: SWN analysis, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swn_counts= fin_data['SWN analysis'].value_counts()\n",
    "swn_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x183bcf21508>,\n",
       "  <matplotlib.patches.Wedge at 0x183bcf25448>,\n",
       "  <matplotlib.patches.Wedge at 0x183bcf260c8>],\n",
       " [Text(-0.23410838641935386, 1.1769423364838767, 'Negative'),\n",
       "  Text(0.14357877740594172, -1.0905893519921306, 'Positive'),\n",
       "  Text(1.0976448112122377, -0.0719435085247544, 'Neutral')],\n",
       " [Text(-0.13656322541128973, 0.6865496962822613, '56.2%'),\n",
       "  Text(0.07831569676687729, -0.5948669192684347, '41.7%'),\n",
       "  Text(0.5987153515703113, -0.039241913740775125, '2.1%')])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "swn_counts= fin_data['SWN analysis'].value_counts()\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.pie(swn_counts.values, labels = swn_counts.index, explode = (0.1, 0, 0), autopct='%1.1f%%', shadow=False)\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual representation of TextBlob, VADER, SentiWordNet results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x183c7a19948>,\n",
       "  <matplotlib.patches.Wedge at 0x183c7a4d948>,\n",
       "  <matplotlib.patches.Wedge at 0x183c7a51e08>],\n",
       " [Text(-0.21459935421774104, 1.0788638084435536, 'Negative'),\n",
       "  Text(0.14357877740594172, -1.0905893519921306, 'Positive'),\n",
       "  Text(1.3471095410332008, -0.08829430591674403, 'Neutral')],\n",
       " [Text(-0.11705419320967692, 0.5884711682419382, '56.2%'),\n",
       "  Text(0.07831569676687729, -0.5948669192684347, '41.7%'),\n",
       "  Text(0.8481800813912743, -0.05559271113276476, '2.1%')])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title(\"TextBlob results\")\n",
    "plt.pie(tb_counts.values, labels = tb_counts.index, explode = (0, 0, 0.25), autopct='%1.1f%%', shadow=False)\n",
    "plt.subplot(1,3,2)\n",
    "plt.title(\"VADER results\")\n",
    "plt.pie(vader_counts.values, labels = vader_counts.index, explode = (0, 0, 0.25), autopct='%1.1f%%', shadow=False)\n",
    "plt.subplot(1,3,3)\n",
    "plt.title(\"SentiWordNet results\")\n",
    "plt.pie(swn_counts.values, labels = swn_counts.index, explode = (0, 0, 0.25), autopct='%1.1f%%', shadow=False)"
   ]
  }
 ],
 "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
}