Amazon Sentiment Analysis (In Progress).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bz2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chardet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Activation, Dropout\n",
    "from keras import optimizers\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = open(\"./train.ft.txt\")\n",
    "train_lines = train.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'__label__2 Stuning even for the non-gamer: This sound track was beautiful! It paints the senery in your mind so well I would recomend it even to people who hate vid. game music! I have played the game Chrono Cross but out of all of the games I have ever played it has the best music! It backs away from crude keyboarding and takes a fresher step with grate guitars and soulful orchestras. It would impress anyone who cares to listen! ^_^\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_lines[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decoding the Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = [0 if x.split(' ')[0] == '__label__1' else 1 for x in train_lines]\n",
    "train_sentences = [x.split(' ', 1)[1][:-1].lower() for x in train_lines]\n",
    "\n",
    "for i in range(len(train_sentences)):\n",
    "    train_sentences[i] = re.sub('\\d','0',train_sentences[i])\n",
    "                                                           \n",
    "for i in range(len(train_sentences)):\n",
    "    if 'www.' in train_sentences[i] or 'http:' in train_sentences[i] or 'https:' in train_sentences[i] or '.com' in train_sentences[i]:\n",
    "        train_sentences[i] = re.sub(r\"([^ ]+(?<=\\.[a-z]{3}))\", \"<url>\", train_sentences[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3600000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'great resource for chinese/eastern medincine: this is an excellent book to have as a reference fo eastern methodologies in medicine and healing. unfortunately it is long out of print, but if you can get your hands on a copy of this text, you will not be dissappointed. in fact, if you are unhappy with it, i will be happy to purchase it from you as i have many peers that would happily use it.'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sentences[192]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Translating the Sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "for s in range(len(train_sentences)):\n",
    "    train_sentences[s] = train_sentences[s].translate(str.maketrans('','',string.punctuation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'great resource for chineseeastern medincine this is an excellent book to have as a reference fo eastern methodologies in medicine and healing unfortunately it is long out of print but if you can get your hands on a copy of this text you will not be dissappointed in fact if you are unhappy with it i will be happy to purchase it from you as i have many peers that would happily use it'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sentences[192]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at Other Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'smoothing serum the product is wonderful for my hair which is very curly and tends to be frizzy smooths it and gets rid of allof the friz use it once a day even in damp weather'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sentences[205]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.DataFrame(data=list(zip(train_sentences, train_labels)), \n",
    "                     columns=['review_text', 'sentiment_class_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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_text</th>\n",
       "      <th>sentiment_class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>stuning even for the nongamer this sound track...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>the best soundtrack ever to anything im readin...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amazing this soundtrack is my favorite music o...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>excellent soundtrack i truly like this soundtr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>remember pull your jaw off the floor after hea...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         review_text  sentiment_class_label\n",
       "0  stuning even for the nongamer this sound track...                      1\n",
       "1  the best soundtrack ever to anything im readin...                      1\n",
       "2  amazing this soundtrack is my favorite music o...                      1\n",
       "3  excellent soundtrack i truly like this soundtr...                      1\n",
       "4  remember pull your jaw off the floor after hea...                      1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['word_count'] = [len(text.split()) for text in train.review_text]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking the Data Again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_text</th>\n",
       "      <th>sentiment_class_label</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>textbook book shipped quickly and was in excel...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>janes all the worlds aircraft 00000 great to d...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>edge of danger 0 star  only because thats the ...</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>needs  upgrade only has limited access to obdi...</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>good read            good read                ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           review_text  sentiment_class_label  \\\n",
       "100  textbook book shipped quickly and was in excel...                      1   \n",
       "669  janes all the worlds aircraft 00000 great to d...                      1   \n",
       "686  edge of danger 0 star  only because thats the ...                      0   \n",
       "725  needs  upgrade only has limited access to obdi...                      0   \n",
       "881  good read            good read                ...                      1   \n",
       "\n",
       "     word_count  \n",
       "100          16  \n",
       "669          18  \n",
       "686          17  \n",
       "725          19  \n",
       "881           4  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29876, 3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering Words that are Less than 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29876, 3)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train[train.word_count < 30]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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_text</th>\n",
       "      <th>sentiment_class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>textbook book shipped quickly and was in excel...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>janes all the worlds aircraft 00000 great to d...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>edge of danger 0 star  only because thats the ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>needs  upgrade only has limited access to obdi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>good read            good read                ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                           review_text  sentiment_class_label\n",
       "100  textbook book shipped quickly and was in excel...                      1\n",
       "669  janes all the worlds aircraft 00000 great to d...                      1\n",
       "686  edge of danger 0 star  only because thats the ...                      0\n",
       "725  needs  upgrade only has limited access to obdi...                      0\n",
       "881  good read            good read                ...                      1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.drop(columns=['word_count'], axis=1)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.set_index(np.arange(len(train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Review Text for the Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp={}\n",
    "for t in train.review_text:\n",
    "    for s in t.split():\n",
    "        if s in mp:\n",
    "            mp[s]+=1\n",
    "        else:\n",
    "            mp[s]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list=[]\n",
    "for key, value in mp.items():\n",
    "    if value>5:\n",
    "        word_list.append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'textbook': 45,\n",
       " 'book': 5281,\n",
       " 'shipped': 241,\n",
       " 'quickly': 465,\n",
       " 'and': 12773,\n",
       " 'was': 4848,\n",
       " 'in': 5136,\n",
       " 'excellent': 2812,\n",
       " 'condition': 1029,\n",
       " 'as': 2541,\n",
       " 'stated': 83,\n",
       " 'easy': 995,\n",
       " 'transaction': 181,\n",
       " 'would': 1361,\n",
       " 'buy': 1353,\n",
       " 'again': 877,\n",
       " 'janes': 1,\n",
       " 'all': 1777,\n",
       " 'the': 19105,\n",
       " 'worlds': 16,\n",
       " 'aircraft': 7,\n",
       " '00000': 78,\n",
       " 'great': 7478,\n",
       " 'to': 8100,\n",
       " 'deal': 166,\n",
       " 'with': 3836,\n",
       " 'very': 5321,\n",
       " 'quick': 316,\n",
       " 'delivery': 497,\n",
       " 'highly': 696,\n",
       " 'recommended': 457,\n",
       " 'thank': 298,\n",
       " 'you': 2879,\n",
       " 'edge': 29,\n",
       " 'of': 7534,\n",
       " 'danger': 8,\n",
       " '0': 1325,\n",
       " 'star': 129,\n",
       " 'only': 802,\n",
       " 'because': 289,\n",
       " 'thats': 175,\n",
       " 'minimumthis': 1,\n",
       " 'proves': 22,\n",
       " 'famous': 26,\n",
       " 'can': 767,\n",
       " 'publish': 9,\n",
       " 'anything': 236,\n",
       " 'needs': 169,\n",
       " 'upgrade': 31,\n",
       " 'has': 1090,\n",
       " 'limited': 60,\n",
       " 'access': 22,\n",
       " 'obdii': 1,\n",
       " 'data': 27,\n",
       " 'stream': 8,\n",
       " 'unless': 61,\n",
       " 'pay': 64,\n",
       " 'more': 1228,\n",
       " 'money': 1062,\n",
       " 'software': 92,\n",
       " 'good': 4690,\n",
       " 'read': 1194,\n",
       " 'a': 10781,\n",
       " 'disappointment': 206,\n",
       " 'maeves': 1,\n",
       " 'forte': 1,\n",
       " 'is': 8980,\n",
       " 'character': 89,\n",
       " 'development': 49,\n",
       " 'dialogue': 46,\n",
       " 'interaction': 9,\n",
       " 'silver': 27,\n",
       " 'wedding': 25,\n",
       " 'far': 192,\n",
       " 'below': 43,\n",
       " 'her': 342,\n",
       " 'usual': 51,\n",
       " 'standard': 75,\n",
       " 'fridas': 1,\n",
       " 'attractive': 49,\n",
       " 'soundtrack': 132,\n",
       " 'sister': 21,\n",
       " 'utterly': 39,\n",
       " 'amazing': 439,\n",
       " 'unique': 73,\n",
       " 'sound': 537,\n",
       " 'voice': 205,\n",
       " 'so': 1179,\n",
       " 'powerful': 94,\n",
       " 'awesome': 658,\n",
       " 'what': 1525,\n",
       " 'i': 8694,\n",
       " 'say': 318,\n",
       " 'about': 1077,\n",
       " 'this': 11968,\n",
       " 'album': 803,\n",
       " 'that': 1984,\n",
       " 'hasnt': 33,\n",
       " 'already': 158,\n",
       " 'been': 475,\n",
       " 'said': 195,\n",
       " 'nothing': 493,\n",
       " 'on': 2604,\n",
       " 'every': 407,\n",
       " 'level': 78,\n",
       " 'bernie': 1,\n",
       " 'sings': 15,\n",
       " 'too': 1021,\n",
       " 'be': 1531,\n",
       " 'sure': 136,\n",
       " 'nice': 957,\n",
       " 'cd': 1027,\n",
       " 'from': 1769,\n",
       " 'long': 289,\n",
       " 'years': 233,\n",
       " 'agobut': 1,\n",
       " 'especially': 241,\n",
       " 'for': 6933,\n",
       " 'elton': 7,\n",
       " 'john': 70,\n",
       " 'fans': 132,\n",
       " 'wishmaster': 1,\n",
       " 'fantastic': 399,\n",
       " 'should': 593,\n",
       " 'grace': 20,\n",
       " 'everyones': 12,\n",
       " 'music': 996,\n",
       " 'collection': 439,\n",
       " 'innovating': 1,\n",
       " 'always': 318,\n",
       " 'refreshing': 46,\n",
       " 'truly': 156,\n",
       " 'masterpiece': 83,\n",
       " 'like': 1687,\n",
       " 'critics': 6,\n",
       " 'riveting': 16,\n",
       " 'cant': 499,\n",
       " 'wait': 157,\n",
       " 'next': 149,\n",
       " 'one': 1777,\n",
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       " 'songs': 429,\n",
       " 'artist': 61,\n",
       " 'clay': 10,\n",
       " 'aiken': 5,\n",
       " 'kind': 85,\n",
       " 'brilliant': 151,\n",
       " 'vocal': 25,\n",
       " 'tedious': 36,\n",
       " 'debut': 21,\n",
       " 'silly': 53,\n",
       " 'syrupy': 3,\n",
       " 'lyrics': 118,\n",
       " 'throughout': 45,\n",
       " 'surprisingly': 22,\n",
       " 'vocals': 72,\n",
       " 'uninspired': 15,\n",
       " 'wanted': 157,\n",
       " 'start': 102,\n",
       " 'sort': 24,\n",
       " 'slumps': 1,\n",
       " 'downward': 2,\n",
       " 'never': 760,\n",
       " 'comes': 97,\n",
       " 'backmight': 1,\n",
       " 'ok': 206,\n",
       " 'reference': 173,\n",
       " 'triumvirat': 1,\n",
       " 'grew': 9,\n",
       " 'up': 657,\n",
       " 'members': 8,\n",
       " 'disco': 18,\n",
       " 'sucks': 147,\n",
       " 'documentary': 61,\n",
       " 'documentation': 16,\n",
       " 'peoples': 16,\n",
       " 'temple': 7,\n",
       " 'jonestown': 1,\n",
       " 'tragedy': 7,\n",
       " 'uihuih': 1,\n",
       " 'uyytui': 1,\n",
       " 'gyuh': 1,\n",
       " 'k': 8,\n",
       " 'èyui': 1,\n",
       " 'hjuyuy': 1,\n",
       " 'ihuhui': 1,\n",
       " 'jhihui': 1,\n",
       " 'jhuiytfrty': 1,\n",
       " 'rere': 1,\n",
       " 'ddszq': 1,\n",
       " 'edss': 1,\n",
       " 'ezqf': 1,\n",
       " 'gdret': 1,\n",
       " 'trdredtr': 1,\n",
       " 'drez': 1,\n",
       " 'd': 20,\n",
       " 'average': 66,\n",
       " 'shirts': 10,\n",
       " 'qualityworth': 3,\n",
       " '0000built': 1,\n",
       " 'stays': 19,\n",
       " 'bland': 31,\n",
       " 'spent': 46,\n",
       " 'two': 350,\n",
       " 'pages': 107,\n",
       " 'listing': 46,\n",
       " 'sponges': 3,\n",
       " 'solid': 110,\n",
       " 'children': 177,\n",
       " 'upany': 1,\n",
       " 'younger': 17,\n",
       " 'may': 91,\n",
       " 'grasp': 6,\n",
       " 'concept': 49,\n",
       " 'light': 230,\n",
       " 'weight': 83,\n",
       " 'facial': 4,\n",
       " 'sunscreenand': 1,\n",
       " 'day': 168,\n",
       " 'moisturizernot': 1,\n",
       " 'heavy': 135,\n",
       " 'gooey': 1,\n",
       " 'sunscreens': 1,\n",
       " 'fuzzy': 12,\n",
       " 'logic': 14,\n",
       " 'disprovensic': 1,\n",
       " 'example': 52,\n",
       " 'prove': 7,\n",
       " 'isnt': 111,\n",
       " 'tooth': 9,\n",
       " 'fairy': 11,\n",
       " 'bookgreatest': 1,\n",
       " 'tips': 44,\n",
       " 'tricks': 12,\n",
       " 'tweakings': 1,\n",
       " 'growersdefinitely': 1,\n",
       " 'cannabis': 1,\n",
       " 'grow': 14,\n",
       " 'documenting': 2,\n",
       " 'quicklydecreasing': 1,\n",
       " 'generation': 32,\n",
       " 'those': 253,\n",
       " 'lived': 14,\n",
       " 'through': 240,\n",
       " 'wwii': 9,\n",
       " 'brick': 8,\n",
       " 'carry': 27,\n",
       " 'surface': 18,\n",
       " 'rather': 110,\n",
       " 'shoot': 15,\n",
       " 'myself': 45,\n",
       " 'lean': 7,\n",
       " 'rice': 31,\n",
       " 'cooker': 22,\n",
       " 'once': 118,\n",
       " 'appliances': 6,\n",
       " 'owned': 26,\n",
       " 'fair': 52,\n",
       " 'goodsize': 1,\n",
       " 'goodrange': 1,\n",
       " 'noise': 71,\n",
       " 'roombattery': 1,\n",
       " 'life': 313,\n",
       " 'computernice': 1,\n",
       " 'screen': 71,\n",
       " 'computerblue': 1,\n",
       " 'maniabreathes': 1,\n",
       " 'heavyoverallbad': 1,\n",
       " 'computerdont': 1,\n",
       " 'self': 45,\n",
       " 'spongebob': 12,\n",
       " 'absolutely': 370,\n",
       " 'spongeboband': 1,\n",
       " 'waithooray': 1,\n",
       " 'patrick': 6,\n",
       " 'dis': 4,\n",
       " 'ssuucckkss': 1,\n",
       " 'theter': 1,\n",
       " 'blew': 10,\n",
       " 'chunks': 1,\n",
       " 'spongbob': 1,\n",
       " 'friends': 113,\n",
       " 'gay': 7,\n",
       " 'youre': 128,\n",
       " 'saying': 20,\n",
       " 'him': 71,\n",
       " 'hes': 45,\n",
       " 'optimistic': 2,\n",
       " 'show': 214,\n",
       " 'safe': 28,\n",
       " 'timely': 265,\n",
       " 'manner': 162,\n",
       " 'evergrowing': 1,\n",
       " 'library': 70,\n",
       " 'antique': 4,\n",
       " 'smooth': 87,\n",
       " 'age': 57,\n",
       " 'unsubstantiated': 3,\n",
       " 'vague': 18,\n",
       " 'unscientifically': 1,\n",
       " 'presented': 42,\n",
       " 'sticky': 21,\n",
       " 'yucky': 6,\n",
       " 'mess': 37,\n",
       " 'deodorant': 3,\n",
       " 'control': 59,\n",
       " 'gluing': 1,\n",
       " 'pits': 4,\n",
       " 'shut': 7,\n",
       " 'seriously': 48,\n",
       " 'stuff': 258,\n",
       " ...}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5906"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looping the Word List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat_inform=[]\n",
    "for i in train.iterrows():\n",
    "    lis=[]\n",
    "    text = i[1]['review_text'].split()\n",
    "    for j in word_list:\n",
    "        if j in text:\n",
    "            lis.append(1)\n",
    "        else:\n",
    "            lis.append(0)\n",
    "            \n",
    "            \n",
    "    dat_inform.append(lis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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