Sentiment_analysis_Classifiers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 878,
     "status": "ok",
     "timestamp": 1528556219743,
     "user": {
      "displayName": "Sauradeep Chakraborty",
      "photoUrl": "//lh6.googleusercontent.com/-QvGcv_ohGQE/AAAAAAAAAAI/AAAAAAAADh8/JnoW4qEexCc/s50-c-k-no/photo.jpg",
      "userId": "104205094747188221458"
     },
     "user_tz": -480
    },
    "id": "VVJnbioWUkKR",
    "outputId": "bfd11a10-2724-4aa5-95fb-843909f312e2"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import time\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, LSTM\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import re\n",
    "from gensim.models import Doc2Vec, doc2vec\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import *\n",
    "from textblob import TextBlob, Word\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from nltk import tokenize\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196884e5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\", dtype={\"label\": int, \"text\": str})\n",
    "p = df.groupby(\"label\").count()\n",
    "\n",
    "plt.figure(0)\n",
    "plt.bar(p.index, p.text, alpha=0.5)\n",
    "plt.xticks(p.index)\n",
    "plt.xlabel(\"Sentiment\")\n",
    "plt.ylabel(\"No. of Reviews\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1146
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 107160,
     "status": "ok",
     "timestamp": 1528556333102,
     "user": {
      "displayName": "Sauradeep Chakraborty",
      "photoUrl": "//lh6.googleusercontent.com/-QvGcv_ohGQE/AAAAAAAAAAI/AAAAAAAADh8/JnoW4qEexCc/s50-c-k-no/photo.jpg",
      "userId": "104205094747188221458"
     },
     "user_tz": -480
    },
    "id": "lPdSSHPwUrSn",
    "outputId": "4f0ddb88-f2d9-46e6-8558-cbdec9fb52e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "682.8966610431671\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[great, mobil, app, nice, reward, program, mak...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[realli, fast, polit, definit, recommend, also...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>[alway, amaz, friendli, staff, great, deal, pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>[wine, 101, class, friday, night, cool, spot, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>[round, think, may, potenti, coupl, thing, tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>[romant, tryst, planet, hollywood, poker, room...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>[got, color, haircut, final, month, look, righ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>[never, go, nearli, empti, 430, pm, sat, way, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>[ive, come, 2, year, np, bought, staff, alway,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>[07082014, war, ich, mit, 5, kollegen, mittag,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>[5, star, takeout, not, recommend, dinein, no,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>[nachdem, wir, jetzt, da, dritt, mal, vor, der...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>[hand, best, polish, boy, sandwich, cleveland,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>[oh, fuzzi, begin, familiar, tobi, keith, song...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>[ive, tri, mani, cupcak, magnolia, prari, girl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>[put, irish, pub, walmart, youd, get, dont, ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>[amaz, pho, almost, drink, broth, straw, lol, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>[la, eiffel, tower, en, la, vegasposta, eso, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>[love, shop, best, retro, dress, vega, girl, w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>[definit, sexi, show, surprisingli, funni, boy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>[someth, must, gone, horribl, wrong, sinc, las...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>[happi, find, silk, found, nicol, profession, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>[good, get, qdoba, serious, qdoba, good, tast,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>[anoth, one, favorit, spot, awesom, pregam, 3,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>[count, charleston, topnotch, ambienc, visit, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>[amaz, went, massag, relax, staff, profession]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>[man, oh, man, fuck, bomb, pastor, asada, taco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>[great, big, portion, excel, custom, dont, car...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>[son, iphon, 6, screen, bust, fix, less, hour,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>[ye, space, cozi, comfi, le, club, chass, et, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399971</th>\n",
       "      <td>0</td>\n",
       "      <td>[came, 12pm, tuesday, theyr, suppos, open, 10a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399972</th>\n",
       "      <td>2</td>\n",
       "      <td>[pizza, delici, daughter, love, big, rush, acc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399973</th>\n",
       "      <td>2</td>\n",
       "      <td>[love, wait, row, half, hour, definit, worth, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399974</th>\n",
       "      <td>2</td>\n",
       "      <td>[walk, 5, day, week, sinc, novemb, 11th, today...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399975</th>\n",
       "      <td>0</td>\n",
       "      <td>[dont, usual, write, 1, star, moira, deserv, o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399976</th>\n",
       "      <td>1</td>\n",
       "      <td>[mediocr, overpr, buffet, come, sushi, havent,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399977</th>\n",
       "      <td>1</td>\n",
       "      <td>[fantast, though, pricier, side, brunch, good,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399978</th>\n",
       "      <td>0</td>\n",
       "      <td>[ok, c, 25, quick, arriv, wait, 12, minut, ord...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399979</th>\n",
       "      <td>0</td>\n",
       "      <td>[la, vega, past, weekend, attend, mountain, we...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399980</th>\n",
       "      <td>2</td>\n",
       "      <td>[whatev, need, done, subaru, guy, reliabl, exp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399981</th>\n",
       "      <td>2</td>\n",
       "      <td>[fantast, experi, room, inde, whole, hotel, cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399982</th>\n",
       "      <td>2</td>\n",
       "      <td>[well, first, offici, verizon, wireless, store...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399983</th>\n",
       "      <td>1</td>\n",
       "      <td>[got, vanilla, spice, oatmeal, 8, like, 4, sli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399984</th>\n",
       "      <td>2</td>\n",
       "      <td>[delight, eleg, tast, afford, overal, great, e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399985</th>\n",
       "      <td>2</td>\n",
       "      <td>[stay, sever, time, manag, staff, polit, help,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399986</th>\n",
       "      <td>2</td>\n",
       "      <td>[great, chines, great, take, friend, not, bad,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399987</th>\n",
       "      <td>2</td>\n",
       "      <td>[never, 188, forev, continu, come, back, alli,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399988</th>\n",
       "      <td>1</td>\n",
       "      <td>[perfectli, adequ, gyro, small, clean, enough,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399989</th>\n",
       "      <td>2</td>\n",
       "      <td>[ive, use, mbv, least, nine, year, fix, 2000, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399990</th>\n",
       "      <td>0</td>\n",
       "      <td>[dont, bother, come, back, hadnt, 10, year, ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399991</th>\n",
       "      <td>2</td>\n",
       "      <td>[couldnt, happier, 5th, wine, first, introduc,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399992</th>\n",
       "      <td>2</td>\n",
       "      <td>[kasade, fantast, hair, stylist, best, ive, fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399993</th>\n",
       "      <td>1</td>\n",
       "      <td>[decid, grab, fish, chip, thought, not, get, b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399994</th>\n",
       "      <td>1</td>\n",
       "      <td>[hot, dog, 1218, inch, great, frozen, hot, cho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399995</th>\n",
       "      <td>2</td>\n",
       "      <td>[never, use, move, compani, prior, famili, mov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399996</th>\n",
       "      <td>2</td>\n",
       "      <td>[went, first, time, got, kelsey, dye, cut, hai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399997</th>\n",
       "      <td>2</td>\n",
       "      <td>[first, experi, true, kitchen, trip, scottsdal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399998</th>\n",
       "      <td>2</td>\n",
       "      <td>[must, one, talkedabout, secondhand, bike, pla...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399999</th>\n",
       "      <td>2</td>\n",
       "      <td>[undeni, best, bang, buck, citi, 10, buck, get...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400000</th>\n",
       "      <td>0</td>\n",
       "      <td>[dont, order, hamburg, order, well, done, wait...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label                                               text\n",
       "0           1  [great, mobil, app, nice, reward, program, mak...\n",
       "1           2  [realli, fast, polit, definit, recommend, also...\n",
       "2           2  [alway, amaz, friendli, staff, great, deal, pr...\n",
       "3           1  [wine, 101, class, friday, night, cool, spot, ...\n",
       "4           1  [round, think, may, potenti, coupl, thing, tri...\n",
       "5           0  [romant, tryst, planet, hollywood, poker, room...\n",
       "6           2  [got, color, haircut, final, month, look, righ...\n",
       "7           0  [never, go, nearli, empti, 430, pm, sat, way, ...\n",
       "8           2  [ive, come, 2, year, np, bought, staff, alway,...\n",
       "9           0  [07082014, war, ich, mit, 5, kollegen, mittag,...\n",
       "10          2  [5, star, takeout, not, recommend, dinein, no,...\n",
       "11          1  [nachdem, wir, jetzt, da, dritt, mal, vor, der...\n",
       "12          2  [hand, best, polish, boy, sandwich, cleveland,...\n",
       "13          2  [oh, fuzzi, begin, familiar, tobi, keith, song...\n",
       "14          2  [ive, tri, mani, cupcak, magnolia, prari, girl...\n",
       "15          1  [put, irish, pub, walmart, youd, get, dont, ge...\n",
       "16          2  [amaz, pho, almost, drink, broth, straw, lol, ...\n",
       "17          1  [la, eiffel, tower, en, la, vegasposta, eso, p...\n",
       "18          2  [love, shop, best, retro, dress, vega, girl, w...\n",
       "19          2  [definit, sexi, show, surprisingli, funni, boy...\n",
       "20          0  [someth, must, gone, horribl, wrong, sinc, las...\n",
       "21          2  [happi, find, silk, found, nicol, profession, ...\n",
       "22          2  [good, get, qdoba, serious, qdoba, good, tast,...\n",
       "23          2  [anoth, one, favorit, spot, awesom, pregam, 3,...\n",
       "24          2  [count, charleston, topnotch, ambienc, visit, ...\n",
       "25          2     [amaz, went, massag, relax, staff, profession]\n",
       "26          2  [man, oh, man, fuck, bomb, pastor, asada, taco...\n",
       "27          1  [great, big, portion, excel, custom, dont, car...\n",
       "28          2  [son, iphon, 6, screen, bust, fix, less, hour,...\n",
       "29          1  [ye, space, cozi, comfi, le, club, chass, et, ...\n",
       "...       ...                                                ...\n",
       "399971      0  [came, 12pm, tuesday, theyr, suppos, open, 10a...\n",
       "399972      2  [pizza, delici, daughter, love, big, rush, acc...\n",
       "399973      2  [love, wait, row, half, hour, definit, worth, ...\n",
       "399974      2  [walk, 5, day, week, sinc, novemb, 11th, today...\n",
       "399975      0  [dont, usual, write, 1, star, moira, deserv, o...\n",
       "399976      1  [mediocr, overpr, buffet, come, sushi, havent,...\n",
       "399977      1  [fantast, though, pricier, side, brunch, good,...\n",
       "399978      0  [ok, c, 25, quick, arriv, wait, 12, minut, ord...\n",
       "399979      0  [la, vega, past, weekend, attend, mountain, we...\n",
       "399980      2  [whatev, need, done, subaru, guy, reliabl, exp...\n",
       "399981      2  [fantast, experi, room, inde, whole, hotel, cl...\n",
       "399982      2  [well, first, offici, verizon, wireless, store...\n",
       "399983      1  [got, vanilla, spice, oatmeal, 8, like, 4, sli...\n",
       "399984      2  [delight, eleg, tast, afford, overal, great, e...\n",
       "399985      2  [stay, sever, time, manag, staff, polit, help,...\n",
       "399986      2  [great, chines, great, take, friend, not, bad,...\n",
       "399987      2  [never, 188, forev, continu, come, back, alli,...\n",
       "399988      1  [perfectli, adequ, gyro, small, clean, enough,...\n",
       "399989      2  [ive, use, mbv, least, nine, year, fix, 2000, ...\n",
       "399990      0  [dont, bother, come, back, hadnt, 10, year, ma...\n",
       "399991      2  [couldnt, happier, 5th, wine, first, introduc,...\n",
       "399992      2  [kasade, fantast, hair, stylist, best, ive, fo...\n",
       "399993      1  [decid, grab, fish, chip, thought, not, get, b...\n",
       "399994      1  [hot, dog, 1218, inch, great, frozen, hot, cho...\n",
       "399995      2  [never, use, move, compani, prior, famili, mov...\n",
       "399996      2  [went, first, time, got, kelsey, dye, cut, hai...\n",
       "399997      2  [first, experi, true, kitchen, trip, scottsdal...\n",
       "399998      2  [must, one, talkedabout, secondhand, bike, pla...\n",
       "399999      2  [undeni, best, bang, buck, citi, 10, buck, get...\n",
       "400000      0  [dont, order, hamburg, order, well, done, wait...\n",
       "\n",
       "[400001 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.label\n",
    "text = df.text\n",
    "\n",
    "tick = time.time()\n",
    "\n",
    "#Convert all text to lower case\n",
    "text = text.apply(lambda x: \" \".join(x.lower() for x in str(x).split()))\n",
    "\n",
    "#Remove all punctuation\n",
    "for i in range(0, len(text)):\n",
    "    text[i] = re.sub(r'[^\\w\\s]', \"\", text[i])\n",
    "\n",
    "#Remove stop words (removes important words! needs modification)\n",
    "stop = stopwords.words(\"english\")\n",
    "stop = stop[:143]\n",
    "stop.remove(\"not\")\n",
    "stop.remove(\"against\")\n",
    "stop.remove(\"no\")\n",
    "stop.append(\"food\")\n",
    "stop.append(\"place\")\n",
    "stop.append(\"service\")\n",
    "text = text.apply(lambda x: \" \".join(x for x in str(x).split() if x not in stop))\n",
    "\n",
    "#Stemming\n",
    "stemmer = PorterStemmer()\n",
    "text = text.apply(lambda x: \" \".join(stemmer.stem(x) for x in str(x).split()))\n",
    "\n",
    "#seperating words into lists\n",
    "for i, line in enumerate(text):\n",
    "  text[i] = line.split()\n",
    "\n",
    "df.text = text\n",
    "text = list(text)\n",
    "print(time.time() - tick)\n",
    "df.to_csv(\"out.csv\", sep=\",\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>label</th>\n",
       "      <th>text</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>['great', 'mobil', 'app', 'nice', 'reward', 'p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>['realli', 'fast', 'polit', 'definit', 'recomm...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>['alway', 'amaz', 'friendli', 'staff', 'great'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>['wine', '101', 'class', 'friday', 'night', 'c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>['round', 'think', 'may', 'potenti', 'coupl', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>['romant', 'tryst', 'planet', 'hollywood', 'po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>['got', 'color', 'haircut', 'final', 'month', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>['never', 'go', 'nearli', 'empti', '430', 'pm'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>['ive', 'come', '2', 'year', 'np', 'bought', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>['07082014', 'war', 'ich', 'mit', '5', 'kolleg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>['5', 'star', 'takeout', 'not', 'recommend', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>['nachdem', 'wir', 'jetzt', 'da', 'dritt', 'ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>['hand', 'best', 'polish', 'boy', 'sandwich', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>['oh', 'fuzzi', 'begin', 'familiar', 'tobi', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>['ive', 'tri', 'mani', 'cupcak', 'magnolia', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>['put', 'irish', 'pub', 'walmart', 'youd', 'ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>['amaz', 'pho', 'almost', 'drink', 'broth', 's...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>['la', 'eiffel', 'tower', 'en', 'la', 'vegaspo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>['love', 'shop', 'best', 'retro', 'dress', 've...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>['definit', 'sexi', 'show', 'surprisingli', 'f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>['someth', 'must', 'gone', 'horribl', 'wrong',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>['happi', 'find', 'silk', 'found', 'nicol', 'p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>['good', 'get', 'qdoba', 'serious', 'qdoba', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>['anoth', 'one', 'favorit', 'spot', 'awesom', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>['count', 'charleston', 'topnotch', 'ambienc',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>['amaz', 'went', 'massag', 'relax', 'staff', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>['man', 'oh', 'man', 'fuck', 'bomb', 'pastor',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>['great', 'big', 'portion', 'excel', 'custom',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>['son', 'iphon', '6', 'screen', 'bust', 'fix',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>['ye', 'space', 'cozi', 'comfi', 'le', 'club',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399971</th>\n",
       "      <td>399971</td>\n",
       "      <td>0</td>\n",
       "      <td>['came', '12pm', 'tuesday', 'theyr', 'suppos',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399972</th>\n",
       "      <td>399972</td>\n",
       "      <td>2</td>\n",
       "      <td>['pizza', 'delici', 'daughter', 'love', 'big',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399973</th>\n",
       "      <td>399973</td>\n",
       "      <td>2</td>\n",
       "      <td>['love', 'wait', 'row', 'half', 'hour', 'defin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399974</th>\n",
       "      <td>399974</td>\n",
       "      <td>2</td>\n",
       "      <td>['walk', '5', 'day', 'week', 'sinc', 'novemb',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399975</th>\n",
       "      <td>399975</td>\n",
       "      <td>0</td>\n",
       "      <td>['dont', 'usual', 'write', '1', 'star', 'moira...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399976</th>\n",
       "      <td>399976</td>\n",
       "      <td>1</td>\n",
       "      <td>['mediocr', 'overpr', 'buffet', 'come', 'sushi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399977</th>\n",
       "      <td>399977</td>\n",
       "      <td>1</td>\n",
       "      <td>['fantast', 'though', 'pricier', 'side', 'brun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399978</th>\n",
       "      <td>399978</td>\n",
       "      <td>0</td>\n",
       "      <td>['ok', 'c', '25', 'quick', 'arriv', 'wait', '1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399979</th>\n",
       "      <td>399979</td>\n",
       "      <td>0</td>\n",
       "      <td>['la', 'vega', 'past', 'weekend', 'attend', 'm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399980</th>\n",
       "      <td>399980</td>\n",
       "      <td>2</td>\n",
       "      <td>['whatev', 'need', 'done', 'subaru', 'guy', 'r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399981</th>\n",
       "      <td>399981</td>\n",
       "      <td>2</td>\n",
       "      <td>['fantast', 'experi', 'room', 'inde', 'whole',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399982</th>\n",
       "      <td>399982</td>\n",
       "      <td>2</td>\n",
       "      <td>['well', 'first', 'offici', 'verizon', 'wirele...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399983</th>\n",
       "      <td>399983</td>\n",
       "      <td>1</td>\n",
       "      <td>['got', 'vanilla', 'spice', 'oatmeal', '8', 'l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399984</th>\n",
       "      <td>399984</td>\n",
       "      <td>2</td>\n",
       "      <td>['delight', 'eleg', 'tast', 'afford', 'overal'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399985</th>\n",
       "      <td>399985</td>\n",
       "      <td>2</td>\n",
       "      <td>['stay', 'sever', 'time', 'manag', 'staff', 'p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399986</th>\n",
       "      <td>399986</td>\n",
       "      <td>2</td>\n",
       "      <td>['great', 'chines', 'great', 'take', 'friend',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399987</th>\n",
       "      <td>399987</td>\n",
       "      <td>2</td>\n",
       "      <td>['never', '188', 'forev', 'continu', 'come', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399988</th>\n",
       "      <td>399988</td>\n",
       "      <td>1</td>\n",
       "      <td>['perfectli', 'adequ', 'gyro', 'small', 'clean...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399989</th>\n",
       "      <td>399989</td>\n",
       "      <td>2</td>\n",
       "      <td>['ive', 'use', 'mbv', 'least', 'nine', 'year',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399990</th>\n",
       "      <td>399990</td>\n",
       "      <td>0</td>\n",
       "      <td>['dont', 'bother', 'come', 'back', 'hadnt', '1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399991</th>\n",
       "      <td>399991</td>\n",
       "      <td>2</td>\n",
       "      <td>['couldnt', 'happier', '5th', 'wine', 'first',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399992</th>\n",
       "      <td>399992</td>\n",
       "      <td>2</td>\n",
       "      <td>['kasade', 'fantast', 'hair', 'stylist', 'best...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399993</th>\n",
       "      <td>399993</td>\n",
       "      <td>1</td>\n",
       "      <td>['decid', 'grab', 'fish', 'chip', 'thought', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399994</th>\n",
       "      <td>399994</td>\n",
       "      <td>1</td>\n",
       "      <td>['hot', 'dog', '1218', 'inch', 'great', 'froze...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399995</th>\n",
       "      <td>399995</td>\n",
       "      <td>2</td>\n",
       "      <td>['never', 'use', 'move', 'compani', 'prior', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399996</th>\n",
       "      <td>399996</td>\n",
       "      <td>2</td>\n",
       "      <td>['went', 'first', 'time', 'got', 'kelsey', 'dy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399997</th>\n",
       "      <td>399997</td>\n",
       "      <td>2</td>\n",
       "      <td>['first', 'experi', 'true', 'kitchen', 'trip',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399998</th>\n",
       "      <td>399998</td>\n",
       "      <td>2</td>\n",
       "      <td>['must', 'one', 'talkedabout', 'secondhand', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399999</th>\n",
       "      <td>399999</td>\n",
       "      <td>2</td>\n",
       "      <td>['undeni', 'best', 'bang', 'buck', 'citi', '10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400000</th>\n",
       "      <td>400000</td>\n",
       "      <td>0</td>\n",
       "      <td>['dont', 'order', 'hamburg', 'order', 'well', ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  label                                               text\n",
       "0                0      1  ['great', 'mobil', 'app', 'nice', 'reward', 'p...\n",
       "1                1      2  ['realli', 'fast', 'polit', 'definit', 'recomm...\n",
       "2                2      2  ['alway', 'amaz', 'friendli', 'staff', 'great'...\n",
       "3                3      1  ['wine', '101', 'class', 'friday', 'night', 'c...\n",
       "4                4      1  ['round', 'think', 'may', 'potenti', 'coupl', ...\n",
       "5                5      0  ['romant', 'tryst', 'planet', 'hollywood', 'po...\n",
       "6                6      2  ['got', 'color', 'haircut', 'final', 'month', ...\n",
       "7                7      0  ['never', 'go', 'nearli', 'empti', '430', 'pm'...\n",
       "8                8      2  ['ive', 'come', '2', 'year', 'np', 'bought', '...\n",
       "9                9      0  ['07082014', 'war', 'ich', 'mit', '5', 'kolleg...\n",
       "10              10      2  ['5', 'star', 'takeout', 'not', 'recommend', '...\n",
       "11              11      1  ['nachdem', 'wir', 'jetzt', 'da', 'dritt', 'ma...\n",
       "12              12      2  ['hand', 'best', 'polish', 'boy', 'sandwich', ...\n",
       "13              13      2  ['oh', 'fuzzi', 'begin', 'familiar', 'tobi', '...\n",
       "14              14      2  ['ive', 'tri', 'mani', 'cupcak', 'magnolia', '...\n",
       "15              15      1  ['put', 'irish', 'pub', 'walmart', 'youd', 'ge...\n",
       "16              16      2  ['amaz', 'pho', 'almost', 'drink', 'broth', 's...\n",
       "17              17      1  ['la', 'eiffel', 'tower', 'en', 'la', 'vegaspo...\n",
       "18              18      2  ['love', 'shop', 'best', 'retro', 'dress', 've...\n",
       "19              19      2  ['definit', 'sexi', 'show', 'surprisingli', 'f...\n",
       "20              20      0  ['someth', 'must', 'gone', 'horribl', 'wrong',...\n",
       "21              21      2  ['happi', 'find', 'silk', 'found', 'nicol', 'p...\n",
       "22              22      2  ['good', 'get', 'qdoba', 'serious', 'qdoba', '...\n",
       "23              23      2  ['anoth', 'one', 'favorit', 'spot', 'awesom', ...\n",
       "24              24      2  ['count', 'charleston', 'topnotch', 'ambienc',...\n",
       "25              25      2  ['amaz', 'went', 'massag', 'relax', 'staff', '...\n",
       "26              26      2  ['man', 'oh', 'man', 'fuck', 'bomb', 'pastor',...\n",
       "27              27      1  ['great', 'big', 'portion', 'excel', 'custom',...\n",
       "28              28      2  ['son', 'iphon', '6', 'screen', 'bust', 'fix',...\n",
       "29              29      1  ['ye', 'space', 'cozi', 'comfi', 'le', 'club',...\n",
       "...            ...    ...                                                ...\n",
       "399971      399971      0  ['came', '12pm', 'tuesday', 'theyr', 'suppos',...\n",
       "399972      399972      2  ['pizza', 'delici', 'daughter', 'love', 'big',...\n",
       "399973      399973      2  ['love', 'wait', 'row', 'half', 'hour', 'defin...\n",
       "399974      399974      2  ['walk', '5', 'day', 'week', 'sinc', 'novemb',...\n",
       "399975      399975      0  ['dont', 'usual', 'write', '1', 'star', 'moira...\n",
       "399976      399976      1  ['mediocr', 'overpr', 'buffet', 'come', 'sushi...\n",
       "399977      399977      1  ['fantast', 'though', 'pricier', 'side', 'brun...\n",
       "399978      399978      0  ['ok', 'c', '25', 'quick', 'arriv', 'wait', '1...\n",
       "399979      399979      0  ['la', 'vega', 'past', 'weekend', 'attend', 'm...\n",
       "399980      399980      2  ['whatev', 'need', 'done', 'subaru', 'guy', 'r...\n",
       "399981      399981      2  ['fantast', 'experi', 'room', 'inde', 'whole',...\n",
       "399982      399982      2  ['well', 'first', 'offici', 'verizon', 'wirele...\n",
       "399983      399983      1  ['got', 'vanilla', 'spice', 'oatmeal', '8', 'l...\n",
       "399984      399984      2  ['delight', 'eleg', 'tast', 'afford', 'overal'...\n",
       "399985      399985      2  ['stay', 'sever', 'time', 'manag', 'staff', 'p...\n",
       "399986      399986      2  ['great', 'chines', 'great', 'take', 'friend',...\n",
       "399987      399987      2  ['never', '188', 'forev', 'continu', 'come', '...\n",
       "399988      399988      1  ['perfectli', 'adequ', 'gyro', 'small', 'clean...\n",
       "399989      399989      2  ['ive', 'use', 'mbv', 'least', 'nine', 'year',...\n",
       "399990      399990      0  ['dont', 'bother', 'come', 'back', 'hadnt', '1...\n",
       "399991      399991      2  ['couldnt', 'happier', '5th', 'wine', 'first',...\n",
       "399992      399992      2  ['kasade', 'fantast', 'hair', 'stylist', 'best...\n",
       "399993      399993      1  ['decid', 'grab', 'fish', 'chip', 'thought', '...\n",
       "399994      399994      1  ['hot', 'dog', '1218', 'inch', 'great', 'froze...\n",
       "399995      399995      2  ['never', 'use', 'move', 'compani', 'prior', '...\n",
       "399996      399996      2  ['went', 'first', 'time', 'got', 'kelsey', 'dy...\n",
       "399997      399997      2  ['first', 'experi', 'true', 'kitchen', 'trip',...\n",
       "399998      399998      2  ['must', 'one', 'talkedabout', 'secondhand', '...\n",
       "399999      399999      2  ['undeni', 'best', 'bang', 'buck', 'citi', '10...\n",
       "400000      400000      0  ['dont', 'order', 'hamburg', 'order', 'well', ...\n",
       "\n",
       "[400001 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  pd.read_csv(\"out.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1094
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 744,
     "status": "ok",
     "timestamp": 1528556444709,
     "user": {
      "displayName": "Sauradeep Chakraborty",
      "photoUrl": "//lh6.googleusercontent.com/-QvGcv_ohGQE/AAAAAAAAAAI/AAAAAAAADh8/JnoW4qEexCc/s50-c-k-no/photo.jpg",
      "userId": "104205094747188221458"
     },
     "user_tz": -480
    },
    "id": "YlLdyXcOU5LO",
    "outputId": "9293eab7-24da-4723-db1e-602cfb901128"
   },
   "outputs": [],
   "source": [
    "X = df.text.values\n",
    "Y = df.label.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer().fit(X)\n",
    "X = vectorizer.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 613,
     "status": "ok",
     "timestamp": 1528556504795,
     "user": {
      "displayName": "Sauradeep Chakraborty",
      "photoUrl": "//lh6.googleusercontent.com/-QvGcv_ohGQE/AAAAAAAAAAI/AAAAAAAADh8/JnoW4qEexCc/s50-c-k-no/photo.jpg",
      "userId": "104205094747188221458"
     },
     "user_tz": -480
    },
    "id": "UrRqxUQlXmXf",
    "outputId": "6abcc617-8f41-46c3-86f7-f28ef0c9049a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400001, 247130)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1284,
     "status": "ok",
     "timestamp": 1528528073553,
     "user": {
      "displayName": "Sauradeep Chakraborty",
      "photoUrl": "//lh6.googleusercontent.com/-QvGcv_ohGQE/AAAAAAAAAAI/AAAAAAAADh8/JnoW4qEexCc/s50-c-k-no/photo.jpg",
      "userId": "104205094747188221458"
     },
     "user_tz": -480
    },
    "id": "2i_VKEzbX0c1",
    "outputId": "8779e112-e317-41f4-f82a-fc0e2c57f057"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "nb = MultinomialNB()\n",
    "nb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1367
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6539,
     "status": "error",
     "timestamp": 1528556849112,
     "user": {
      "displayName": "Sauradeep Chakraborty",
      "photoUrl": "//lh6.googleusercontent.com/-QvGcv_ohGQE/AAAAAAAAAAI/AAAAAAAADh8/JnoW4qEexCc/s50-c-k-no/photo.jpg",
      "userId": "104205094747188221458"
     },
     "user_tz": -480
    },
    "id": "dDYKbyEOX23I",
    "outputId": "6334d409-df24-44b3-a352-917ef770f220"
   },
   "outputs": [],
   "source": [
    "prediction = nb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 155706,
     "status": "ok",
     "timestamp": 1528534609267,
     "user": {
      "displayName": "Sauradeep Chakraborty",
      "photoUrl": "//lh6.googleusercontent.com/-QvGcv_ohGQE/AAAAAAAAAAI/AAAAAAAADh8/JnoW4qEexCc/s50-c-k-no/photo.jpg",
      "userId": "104205094747188221458"
     },
     "user_tz": -480
    },
    "id": "9RAYLnkpX53p",
    "outputId": "9de00fe2-df56-4453-e774-26374e28870b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[21031  1715  1469]\n",
      " [ 2285 12801  5327]\n",
      " [  809  2243 72321]]\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.87      0.87     24215\n",
      "          1       0.76      0.63      0.69     20413\n",
      "          2       0.91      0.96      0.94     75373\n",
      "\n",
      "avg / total       0.88      0.88      0.88    120001\n",
      "\n",
      "Accuracy: 0.8846009616586529\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "acc = []\n",
    "print(confusion_matrix(y_test, prediction))\n",
    "print()\n",
    "print(classification_report(y_test, prediction))\n",
    "acc.append(accuracy_score(y_test, prediction))\n",
    "print(\"Accuracy:\",accuracy_score(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_review = [\"ok\", \"hit\", \"miss\", \"decent\"]\n",
    "transformed_review  = vectorizer.transform(positive_review)\n",
    "\n",
    "nb.predict(transformed_review)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.87      0.87     24215\n",
      "          1       0.76      0.63      0.69     20413\n",
      "          2       0.91      0.96      0.94     75373\n",
      "\n",
      "avg / total       0.88      0.88      0.88    120001\n",
      "\n",
      "Accuracy: 0.8846009616586529\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "prediction = lr.predict(x_test)\n",
    "print(classification_report(y_test, prediction))\n",
    "acc.append(accuracy_score(y_test, prediction))\n",
    "print(\"Accuracy:\",accuracy_score(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishaanverma\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd = SGDClassifier()\n",
    "x_train.shape\n",
    "y_train.shape\n",
    "sgd.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8815676536028867\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.8846009616586529, 0.8846009616586529, 0.8815676536028867]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = sgd.predict(x_test)\n",
    "acc.append(accuracy_score(y_test, predictions))\n",
    "print(accuracy_score(y_test, predictions))\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196d43c4048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "x = [0, 1.5, 3]\n",
    "\n",
    "\n",
    "xt = [\"Naive Bayes\", \"Logistic Regression\", \"SGD\"]\n",
    "plt.title(\"Model Accuracy Comparison\")\n",
    "plt.xticks(x, xt)\n",
    "plt.xlabel(\"Model\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.bar(x,acc)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "default_view": {},
   "name": "Sentiment analysis LSTM.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}