Tsne for amazon fine food review.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1212: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "con= sqlite3.connect('database.sqlite')\n",
    "\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score !=3 \"\"\",con)\n",
    "\n",
    "def partition(x):\n",
    "    if x < 3:\n",
    "        return 'negative'\n",
    "    return 'positive'\n",
    "\n",
    "actualScore = filtered_data['Score']\n",
    "positiveNegative=actualScore.map(partition)\n",
    "filtered_data['Score']= positiveNegative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525814, 10)\n"
     ]
    }
   ],
   "source": [
    "print(filtered_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>negative</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>negative</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>positive</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator     Score        Time  \\\n",
       "0                     1                       1  positive  1303862400   \n",
       "1                     0                       0  negative  1346976000   \n",
       "2                     1                       1  positive  1219017600   \n",
       "3                     3                       3  negative  1307923200   \n",
       "4                     0                       0  positive  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    443777\n",
       "negative     82037\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78445</td>\n",
       "      <td>B000HDL1RQ</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138317</td>\n",
       "      <td>B000HDOPYC</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138277</td>\n",
       "      <td>B000HDOPYM</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73791</td>\n",
       "      <td>B000HDOPZG</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155049</td>\n",
       "      <td>B000PAQ75C</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id   ProductId         UserId      ProfileName  HelpfulnessNumerator  \\\n",
       "0   78445  B000HDL1RQ  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "1  138317  B000HDOPYC  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "2  138277  B000HDOPYM  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "3   73791  B000HDOPZG  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "4  155049  B000PAQ75C  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "\n",
       "   HelpfulnessDenominator  Score        Time  \\\n",
       "0                       2      5  1199577600   \n",
       "1                       2      5  1199577600   \n",
       "2                       2      5  1199577600   \n",
       "3                       2      5  1199577600   \n",
       "4                       2      5  1199577600   \n",
       "\n",
       "                             Summary  \\\n",
       "0  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "1  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "2  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "3  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "4  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "\n",
       "                                                Text  \n",
       "0  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "1  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "2  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "3  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "4  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display= pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3 AND UserId=\"AR5J8UI46CURR\"\n",
    "ORDER BY ProductID\n",
    "\"\"\", con)\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data=filtered_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364173, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final=sorted_data.drop_duplicates(subset={\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.25890143662969"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(final['Id'].size*1.0)/(filtered_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64422</td>\n",
       "      <td>B000MIDROQ</td>\n",
       "      <td>A161DK06JJMCYF</td>\n",
       "      <td>J. E. Stephens \"Jeanne\"</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1224892800</td>\n",
       "      <td>Bought This for My Son at College</td>\n",
       "      <td>My son loves spaghetti so I didn't hesitate or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44737</td>\n",
       "      <td>B001EQ55RW</td>\n",
       "      <td>A2V0I904FH7ABY</td>\n",
       "      <td>Ram</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1212883200</td>\n",
       "      <td>Pure cocoa taste with crunchy almonds inside</td>\n",
       "      <td>It was almost a 'love at first bite' - the per...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id   ProductId          UserId              ProfileName  \\\n",
       "0  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n",
       "1  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     3                       1      5  1224892800   \n",
       "1                     3                       2      4  1212883200   \n",
       "\n",
       "                                        Summary  \\\n",
       "0             Bought This for My Son at College   \n",
       "1  Pure cocoa taste with crunchy almonds inside   \n",
       "\n",
       "                                                Text  \n",
       "0  My son loves spaghetti so I didn't hesitate or...  \n",
       "1  It was almost a 'love at first bite' - the per...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display= pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3 AND Id=44737 OR Id=64422\n",
    "ORDER BY ProductID\n",
    "\"\"\", con)\n",
    "\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(364171, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "positive    307061\n",
       "negative     57110\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(final.shape)\n",
    "\n",
    "final['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "I set aside at least an hour each day to read to my son (3 y/o). At this point, I consider myself a connoisseur of children's books and this is one of the best. Santa Clause put this under the tree. Since then, we've read it perpetually and he loves it.<br /><br />First, this book taught him the months of the year.<br /><br />Second, it's a pleasure to read. Well suited to 1.5 y/o old to 4+.<br /><br />Very few children's books are worth owning. Most should be borrowed from the library. This book, however, deserves a permanent spot on your shelf. Sendak's best.\n"
     ]
    }
   ],
   "source": [
    "i=0;\n",
    "for sent in final['Text'].values:\n",
    "    if (len(re.findall('<.*?>', sent))):\n",
    "        print(i)\n",
    "        print(sent)\n",
    "        break;\n",
    "    i += 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'theirs', 'during', 'was', \"aren't\", 'through', 'any', 'yourselves', 'had', 'until', 'for', 'other', 'shan', \"it's\", \"haven't\", 'those', \"didn't\", 'is', 'does', 'below', 'after', 'mightn', 'been', 'doing', 'himself', 'but', 'under', 'shouldn', 'very', 'this', 'where', 'my', \"hasn't\", 'all', 'isn', \"don't\", 'here', \"wouldn't\", 'do', 'by', 'mustn', 've', 'ourselves', 'didn', 'off', 'should', 'yourself', 'just', 'myself', 'only', 'won', 'why', 'your', 'now', 'it', 'wouldn', 'hadn', 't', 'll', 'before', 'an', 's', 'so', 'did', \"shan't\", 'herself', 'has', 'or', 'into', 'ma', 'when', 'o', 'our', 'yours', 'few', 'too', 'if', \"mightn't\", \"won't\", 'him', 'she', 'as', 'what', 'we', 'and', 'not', \"should've\", \"isn't\", 'were', 'above', 'its', \"you're\", 'you', 'being', 'haven', 'over', 'from', 'i', 'own', 'hers', 'again', 'am', \"doesn't\", 'can', 'having', \"that'll\", 'they', 'themselves', 'each', 'hasn', 'me', \"mustn't\", 'be', 'some', 'up', \"you'd\", 'his', 'couldn', \"she's\", \"you've\", 'ain', \"you'll\", 'needn', 'are', 'most', 'these', 'then', 'against', 'ours', 'doesn', \"wasn't\", 'such', 'aren', 'he', 'their', 'no', 'd', 'whom', 'on', 'while', 'out', 'same', \"hadn't\", 'more', 'both', 'have', 'because', 'itself', 'don', 'there', 'wasn', 'between', 'how', 'at', 'in', 'the', 'once', 'which', 'weren', \"needn't\", \"shouldn't\", 'y', 'them', 'than', 'her', \"couldn't\", 'a', 'to', 'down', 'about', 'of', 'that', 'further', 're', 'with', 'nor', 'will', 'm', \"weren't\", 'who'}\n",
      "#######################################\n",
      "tasti\n"
     ]
    }
   ],
   "source": [
    "stop = set(stopwords.words('english')) #set of stopwords\n",
    "sno = nltk.stem.SnowballStemmer('english') #initialising the snowball stemmer\n",
    "\n",
    "def cleanhtml(sentence): #function to clean the word of any html-tags\n",
    "    cleanr = re.compile('<.*?>')\n",
    "    cleantext = re.sub(cleanr, ' ', sentence)\n",
    "    return cleantext\n",
    "def cleanpunc(sentence): #function to clean the word of any punctuation or special characters\n",
    "    cleaned = re.sub(r'[?|!|\\'|\"|#]',r'',sentence)\n",
    "    cleaned = re.sub(r'[.|,|)|(|\\|/]',r' ',cleaned)\n",
    "    return  cleaned\n",
    "print(stop)\n",
    "print('#######################################')\n",
    "print(sno.stem('tasty'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code for implementing step-by-step the checks mentioned in the pre-processing phase\n",
    "# this code takes a while to run as it needs to run on 500k sentences.\n",
    "i=0\n",
    "str1=' '\n",
    "final_string=[]\n",
    "all_positive_words=[] # store words from +ve reviews here\n",
    "all_negative_words=[] # store words from -ve reviews here.\n",
    "s=''\n",
    "for sent in final['Text'].values:\n",
    "    filtered_sentence=[]\n",
    "    #print(sent);\n",
    "    sent=cleanhtml(sent) # remove HTMl tags\n",
    "    for w in sent.split():\n",
    "        for cleaned_words in cleanpunc(w).split():\n",
    "            if((cleaned_words.isalpha()) & (len(cleaned_words)>2)):    \n",
    "                if(cleaned_words.lower() not in stop):\n",
    "                    s=(sno.stem(cleaned_words.lower())).encode('utf8')\n",
    "                    filtered_sentence.append(s)\n",
    "                    if (final['Score'].values)[i] == 'positive': \n",
    "                        all_positive_words.append(s) #list of all words used to describe positive reviews\n",
    "                    if(final['Score'].values)[i] == 'negative':\n",
    "                        all_negative_words.append(s) #list of all words used to describe negative reviews reviews\n",
    "                else:\n",
    "                    continue\n",
    "            else:\n",
    "                continue \n",
    "    #print(filtered_sentence)\n",
    "    str1 = b\" \".join(filtered_sentence) #final string of cleaned words\n",
    "    #print(\"***********************************************************************\")\n",
    "    \n",
    "    final_string.append(str1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "final['CleanedText']=final_string #adding a column of CleanedText which displays the data after pre-processing of the review \n",
    "final['CleanedText']=final['CleanedText'].str.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.head(3) #below the processed review can be seen in the CleanedText Column \n",
    "\n",
    "\n",
    "# store final table into an SQlLite table for future.\n",
    "conn = sqlite3.connect('final.sqlite')\n",
    "c=conn.cursor()\n",
    "conn.text_factory = str\n",
    "final.to_sql('Reviews', conn,  schema=None, if_exists='replace', index=True, index_label=None, chunksize=None, dtype=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking first 1k positive and first 1k negative\n",
    "data_p=final[final['Score']=='positive'].head(n = 1000)\n",
    "data_n=final[final['Score']=='negative'].head(n = 1000)\n",
    "final_2k=pd.concat([data_p, data_n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Score_2k=final_2k['Score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 11)\n",
      "(2000,)\n"
     ]
    }
   ],
   "source": [
    "print(final_2k.shape)\n",
    "print(Score_2k.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "final_counts = count_vect.fit_transform(final_2k['CleanedText'].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of our text BOW vectorizer  (2000, 7125)\n",
      "the number of unique words  7125\n"
     ]
    }
   ],
   "source": [
    "print(\"the type of count vectorizer \",type(final_counts))\n",
    "print(\"the shape of our text BOW vectorizer \",final_counts.get_shape())\n",
    "print(\"the number of unique words \", final_counts.get_shape()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Common Positive Words :  [(b'like', 139429), (b'tast', 129047), (b'good', 112766), (b'flavor', 109624), (b'love', 107357), (b'use', 103888), (b'great', 103870), (b'one', 96726), (b'product', 91033), (b'tri', 86791), (b'tea', 83888), (b'coffe', 78814), (b'make', 75107), (b'get', 72125), (b'food', 64802), (b'would', 55568), (b'time', 55264), (b'buy', 54198), (b'realli', 52715), (b'eat', 52004)]\n",
      "Most Common Negative Words :  [(b'tast', 34585), (b'like', 32330), (b'product', 28218), (b'one', 20569), (b'flavor', 19575), (b'would', 17972), (b'tri', 17753), (b'use', 15302), (b'good', 15041), (b'coffe', 14716), (b'get', 13786), (b'buy', 13752), (b'order', 12871), (b'food', 12754), (b'dont', 11877), (b'tea', 11665), (b'even', 11085), (b'box', 10844), (b'amazon', 10073), (b'make', 9840)]\n"
     ]
    }
   ],
   "source": [
    "freq_dist_positive=nltk.FreqDist(all_positive_words)\n",
    "freq_dist_negative=nltk.FreqDist(all_negative_words)\n",
    "print(\"Most Common Positive Words : \",freq_dist_positive.most_common(20))\n",
    "print(\"Most Common Negative Words : \",freq_dist_negative.most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bi-gram, tri-gram and n-gram\n",
    "\n",
    "#removing stop words like \"not\" should be avoided before building n-grams\n",
    "count_vect = CountVectorizer(ngram_range=(1,2) ) #in scikit-learn\n",
    "final_bigram_counts = count_vect.fit_transform(final_2k['CleanedText'].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (2000, 74930)\n",
      "the number of unique words including both unigrams and bigrams  74930\n"
     ]
    }
   ],
   "source": [
    "print(\"the type of count vectorizer \",type(final_bigram_counts))\n",
    "print(\"the shape of out text BOW vectorizer \",final_bigram_counts.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_bigram_counts.get_shape()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2000, 74930)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std_data = StandardScaler(with_mean = False).fit_transform(final_bigram_counts)\n",
    "std_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(std_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert sparse to dense as tsne takes dense vector\n",
    "\n",
    "std_data = std_data.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(std_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 511.75x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "model = TSNE(n_components=2, random_state=0, perplexity = 30, n_iter = 5000)\n",
    "# configuring the parameteres\n",
    "# the number of components = 2\n",
    "# default perplexity = 30\n",
    "# default learning rate = 200\n",
    "# default Maximum number of iterations for the optimization = 1000\n",
    "\n",
    "tsne_data = model.fit_transform(std_data)\n",
    "\n",
    "\n",
    "# creating a new data frame which help us in ploting the result data\n",
    "tsne_data = np.vstack((tsne_data.T, Score_2k)).T\n",
    "tsne_df = pd.DataFrame(data=tsne_data, columns=(\"dim1\", \"dim2\", \"Score\"))\n",
    "\n",
    "# Ploting the result of tsne\n",
    "sns.FacetGrid(tsne_df, hue=\"Score\", size=6).map(plt.scatter, 'dim1', 'dim2').add_legend()\n",
    "plt.title(\"TSNE for Bag of Words\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF_IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (2000, 74930)\n",
      "the number of unique words including both unigrams and bigrams  74930\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2))\n",
    "final_tf_idf = tf_idf_vect.fit_transform(final_2k['CleanedText'].values)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf.get_shape()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std_data = StandardScaler(with_mean = False).fit_transform(final_tf_idf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 74930)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(std_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_data=std_data.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(std_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 511.75x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "model = TSNE(n_components=2, perplexity = 50)\n",
    "\n",
    "tsne_data = model.fit_transform(std_data)\n",
    "\n",
    "\n",
    "# creating a new data frame which help us in ploting the result data\n",
    "tsne_data = np.vstack((tsne_data.T, Score_2k)).T\n",
    "tsne_df = pd.DataFrame(data=tsne_data, columns=(\"dim1\", \"dim2\", \"Score\"))\n",
    "\n",
    "# Ploting the result of tsne\n",
    "sns.FacetGrid(tsne_df, hue=\"Score\", size=6).map(plt.scatter, 'dim1', 'dim2').add_legend()\n",
    "plt.title(\"TSNE for TF - IDf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "avg w2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "import gensim\n",
    "\n",
    "#train your own w2v model using your own corpus\n",
    "\n",
    "list_of_sent = []\n",
    "for sent in final_2k['Text'].values:\n",
    "    filtered_sentence = []\n",
    "    sent=cleanhtml(sent)\n",
    "    for w in sent.split():\n",
    "        for cleaned_words in cleanpunc(w).split():\n",
    "            if(cleaned_words.isalpha()):    \n",
    "                filtered_sentence.append(cleaned_words.lower())\n",
    "            else:\n",
    "                continue \n",
    "    list_of_sent.append(filtered_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A charming, rhyming book that describes the circumstances under which you eat (or don't) chicken soup with rice, month-by-month. This sounds like the kind of thing kids would make up while they're out of recess and sing over and over until they drive the teachers crazy. It's cute and catchy and sounds really childlike but is skillfully written.\n",
      "*****************************************************************\n",
      "['a', 'charming', 'rhyming', 'book', 'that', 'describes', 'the', 'circumstances', 'under', 'which', 'you', 'eat', 'or', 'dont', 'chicken', 'soup', 'with', 'rice', 'this', 'sounds', 'like', 'the', 'kind', 'of', 'thing', 'kids', 'would', 'make', 'up', 'while', 'theyre', 'out', 'of', 'recess', 'and', 'sing', 'over', 'and', 'over', 'until', 'they', 'drive', 'the', 'teachers', 'crazy', 'its', 'cute', 'and', 'catchy', 'and', 'sounds', 'really', 'childlike', 'but', 'is', 'skillfully', 'written']\n"
     ]
    }
   ],
   "source": [
    "print(final_2k['Text'].values[5])\n",
    "print(\"*****************************************************************\")\n",
    "print(list_of_sent[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_of_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:gensim.models.base_any2vec:consider setting layer size to a multiple of 4 for greater performance\n"
     ]
    }
   ],
   "source": [
    "w2v_model=gensim.models.Word2Vec(list_of_sent,min_count=5,size=50, workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v = w2v_model[w2v_model.wv.vocab]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2900, 50)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2900\n"
     ]
    }
   ],
   "source": [
    "words = list(w2v_model.wv.vocab)\n",
    "print(len(words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('pretty', 0.9985558390617371),\n",
       " ('sweet', 0.9984210729598999),\n",
       " ('quality', 0.9981987476348877),\n",
       " ('nice', 0.9981351494789124),\n",
       " ('tastes', 0.9979822635650635),\n",
       " ('healthy', 0.9979109764099121),\n",
       " ('color', 0.9978674650192261),\n",
       " ('cheap', 0.9978403449058533),\n",
       " ('expensive', 0.9978253245353699),\n",
       " ('true', 0.9978066682815552)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_model.wv.most_similar('tasty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('but', 0.9946737885475159),\n",
       " ('need', 0.9918225407600403),\n",
       " ('buy', 0.9916343092918396),\n",
       " ('really', 0.9911943674087524),\n",
       " ('sure', 0.9910017848014832),\n",
       " ('think', 0.9909423589706421),\n",
       " ('make', 0.9909103512763977),\n",
       " ('wont', 0.9906368851661682),\n",
       " ('may', 0.989059329032898),\n",
       " ('unless', 0.9890059232711792)]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_model.wv.most_similar('like')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "avg w2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in list_of_sent: # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "        except:\n",
    "            pass\n",
    "    sent_vec /= cnt_words\n",
    "    sent_vectors.append(sent_vec)\n",
    "print(len(sent_vectors))\n",
    "print(len(sent_vectors[0]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 511.75x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#tsne\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "model = TSNE(n_components=2, random_state=0, perplexity = 30, n_iter = 5000)\n",
    "\n",
    "tsne_data = model.fit_transform(sent_vectors)\n",
    "\n",
    "tsne_data = np.vstack((tsne_data.T, Score_2k)).T\n",
    "tsne_df = pd.DataFrame(data=tsne_data, columns=(\"dim1\", \"dim2\", \"Score\"))\n",
    "\n",
    "# Ploting the result of tsne\n",
    "sns.FacetGrid(tsne_df, hue=\"Score\", size=6).map(plt.scatter, 'dim1', 'dim2').add_legend()\n",
    "plt.title(\"TSNE for Average Word2vec\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF-IDF w2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_sent_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent: # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    weight_sum =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            # obtain the tf_idfidf of a word in a sentence/review\n",
    "            tf_idf = final_tf_idf[row, tfidf_feat.index(word)]\n",
    "            sent_vec += (vec * tf_idf)\n",
    "            weight_sum += tf_idf\n",
    "        except:\n",
    "            pass\n",
    "    sent_vec /= weight_sum\n",
    "    tfidf_sent_vectors.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To know length of tfidf vector\n",
    "len(tfidf_sent_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       ...,\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(tfidf_sent_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To replace nan with 0 and inf with large finite number\n",
    "\n",
    "tfidf_sent_vectors = np.nan_to_num(tfidf_sent_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.75x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#tsne\n",
    "from sklearn.manifold import TSNE\n",
    "model = TSNE(n_components=2, random_state=0, perplexity = 50, n_iter = 5000)\n",
    "\n",
    "tsne_data = model.fit_transform(tfidf_sent_vectors)\n",
    "\n",
    "tsne_data = np.vstack((tsne_data.T, Score_2k)).T\n",
    "tsne_df = pd.DataFrame(data=tsne_data, columns=(\"dim1\", \"dim2\", \"Score\"))\n",
    "\n",
    "# Ploting the result of tsne\n",
    "sns.FacetGrid(tsne_df, hue=\"Score\", size=7).map(plt.scatter, 'dim1', 'dim2').add_legend()\n",
    "plt.title(\"TSNE for TF-IDF Word2vec\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obsevations:\n",
    "By looking at all tsne reprentation, none of these gives well separated positive and negative reviews."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}