Truncated_SVD on AFFR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jNC8HEoBChWf"
   },
   "source": [
    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews <br>\n",
    "\n",
    "EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/\n",
    "\n",
    "\n",
    "The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>\n",
    "\n",
    "Number of reviews: 568,454<br>\n",
    "Number of users: 256,059<br>\n",
    "Number of products: 74,258<br>\n",
    "Timespan: Oct 1999 - Oct 2012<br>\n",
    "Number of Attributes/Columns in data: 10 \n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review\n",
    "\n",
    "\n",
    "#### Objective:\n",
    "Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).\n",
    "\n",
    "<br>\n",
    "[Q] How to determine if a review is positive or negative?<br>\n",
    "<br> \n",
    "[Ans] We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8wugXnjtChWj"
   },
   "source": [
    "# [1]. Reading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YmKFSMRoChWm"
   },
   "source": [
    "## [1.1] Loading the data\n",
    "\n",
    "The dataset is available in two forms\n",
    "1. .csv file\n",
    "2. SQLite Database\n",
    "\n",
    "In order to load the data, We have used the SQLITE dataset as it is easier to query the data and visualise the data efficiently.\n",
    "<br> \n",
    "\n",
    "Here as we only want to get the global sentiment of the recommendations (positive or negative), we will purposefully ignore all Scores equal to 3. If the score is above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q_rK3P8xChWq"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\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\n",
    "\n",
    "import re\n",
    "# Tutorial about Python regular expressions: https://pymotw.com/2/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": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qGFlKpmUChWz",
    "outputId": "4167ec47-1fc5-47b4-e342-84eb83b1353c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (100000, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1</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>0</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>1</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                        ...                                                                       Text\n",
       "0   1                        ...                          I have bought several of the Vitality canned d...\n",
       "1   2                        ...                          Product arrived labeled as Jumbo Salted Peanut...\n",
       "2   3                        ...                          This is a confection that has been around a fe...\n",
       "\n",
       "[3 rows x 10 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using SQLite Table to read data.\n",
    "con = sqlite3.connect('../input/database.sqlite') \n",
    "\n",
    "# filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "# SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000, will give top 500000 data points\n",
    "# you can change the number to any other number based on your computing power\n",
    "\n",
    "# filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000\"\"\", con) \n",
    "# for tsne assignment you can take 5k data points\n",
    "\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 100000\"\"\", con) \n",
    "\n",
    "# Give reviews with Score>3 a positive rating(1), and reviews with a score<3 a negative rating(0).\n",
    "def partition(x):\n",
    "    if x < 3:\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "#changing reviews with score less than 3 to be positive and vice-versa\n",
    "actualScore = filtered_data['Score']\n",
    "positiveNegative = actualScore.map(partition) \n",
    "filtered_data['Score'] = positiveNegative\n",
    "print(\"Number of data points in our data\", filtered_data.shape)\n",
    "filtered_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oy6FbbqIChW9"
   },
   "outputs": [],
   "source": [
    "display = pd.read_sql_query(\"\"\"\n",
    "SELECT UserId, ProductId, ProfileName, Time, Score, Text, COUNT(*)\n",
    "FROM Reviews\n",
    "GROUP BY UserId\n",
    "HAVING COUNT(*)>1\n",
    "\"\"\", con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VxmpGW-WChXD",
    "outputId": "a58c60e5-15d8-41f7-de60-5ad8396ce0bf",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80668, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Time</th>\n",
       "      <th>Score</th>\n",
       "      <th>Text</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#oc-R115TNMSPFT9I7</td>\n",
       "      <td>B005ZBZLT4</td>\n",
       "      <td>Breyton</td>\n",
       "      <td>1331510400</td>\n",
       "      <td>2</td>\n",
       "      <td>Overall its just OK when considering the price...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#oc-R11D9D7SHXIJB9</td>\n",
       "      <td>B005HG9ESG</td>\n",
       "      <td>Louis E. Emory \"hoppy\"</td>\n",
       "      <td>1342396800</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife has recurring extreme muscle spasms, u...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#oc-R11DNU2NBKQ23Z</td>\n",
       "      <td>B005ZBZLT4</td>\n",
       "      <td>Kim Cieszykowski</td>\n",
       "      <td>1348531200</td>\n",
       "      <td>1</td>\n",
       "      <td>This coffee is horrible and unfortunately not ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#oc-R11O5J5ZVQE25C</td>\n",
       "      <td>B005HG9ESG</td>\n",
       "      <td>Penguin Chick</td>\n",
       "      <td>1346889600</td>\n",
       "      <td>5</td>\n",
       "      <td>This will be the bottle that you grab from the...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#oc-R12KPBODL2B5ZD</td>\n",
       "      <td>B007OSBEV0</td>\n",
       "      <td>Christopher P. Presta</td>\n",
       "      <td>1348617600</td>\n",
       "      <td>1</td>\n",
       "      <td>I didnt like this coffee. Instead of telling y...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               UserId   ...    COUNT(*)\n",
       "0  #oc-R115TNMSPFT9I7   ...           2\n",
       "1  #oc-R11D9D7SHXIJB9   ...           3\n",
       "2  #oc-R11DNU2NBKQ23Z   ...           2\n",
       "3  #oc-R11O5J5ZVQE25C   ...           3\n",
       "4  #oc-R12KPBODL2B5ZD   ...           2\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qVYRWe1nChXK",
    "outputId": "c2da252a-b5b2-4d00-f066-5025a507dab0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Time</th>\n",
       "      <th>Score</th>\n",
       "      <th>Text</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80638</th>\n",
       "      <td>AZY10LLTJ71NX</td>\n",
       "      <td>B001ATMQK2</td>\n",
       "      <td>undertheshrine \"undertheshrine\"</td>\n",
       "      <td>1296691200</td>\n",
       "      <td>5</td>\n",
       "      <td>I bought this 6 pack because for the price tha...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              UserId   ...    COUNT(*)\n",
       "80638  AZY10LLTJ71NX   ...           5\n",
       "\n",
       "[1 rows x 7 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display[display['UserId']=='AZY10LLTJ71NX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bk3oC9YoChXQ",
    "outputId": "a3445fc5-e58e-48c2-bd80-c8bf3c100a96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "393063"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display['COUNT(*)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tUdW-oABChXZ"
   },
   "source": [
    "#  [2] Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1lWaQ1eFChXb"
   },
   "source": [
    "## [2.1] Data Cleaning: Deduplication\n",
    "\n",
    "It is observed (as shown in the table below) that the reviews data had many duplicate entries. Hence it was necessary to remove duplicates in order to get unbiased results for the analysis of the data.  Following is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0scKI9A4ChXd",
    "outputId": "64eefd15-d63e-4742-a669-6ab413c64157",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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                        ...                                                                       Text\n",
       "0   78445                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "1  138317                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "2  138277                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "3   73791                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "4  155049                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 80,
     "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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ORBstW7kChXk"
   },
   "source": [
    "As it can be seen above that same user has multiple reviews with same values for HelpfulnessNumerator, HelpfulnessDenominator, Score, Time, Summary and Text and on doing analysis it was found that <br>\n",
    "<br> \n",
    "ProductId=B000HDOPZG was Loacker Quadratini Vanilla Wafer Cookies, 8.82-Ounce Packages (Pack of 8)<br>\n",
    "<br> \n",
    "ProductId=B000HDL1RQ was Loacker Quadratini Lemon Wafer Cookies, 8.82-Ounce Packages (Pack of 8) and so on<br>\n",
    "\n",
    "It was inferred after analysis that reviews with same parameters other than ProductId belonged to the same product just having different flavour or quantity. Hence in order to reduce redundancy it was decided to eliminate the rows having same parameters.<br>\n",
    "\n",
    "The method used for the same was that we first sort the data according to ProductId and then just keep the first similar product review and delelte the others. for eg. in the above just the review for ProductId=B000HDL1RQ remains. This method ensures that there is only one representative for each product and deduplication without sorting would lead to possibility of different representatives still existing for the same product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QjqRsNlrChXn"
   },
   "outputs": [],
   "source": [
    "#Sorting data according to ProductId in ascending order\n",
    "sorted_data=filtered_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5f7tJRTNChXr",
    "outputId": "8b6147b9-2eb1-4af5-facc-9bcf1302969d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87775, 10)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deduplication of entries\n",
    "final=sorted_data.drop_duplicates(subset={\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OkMII7G4ChXx",
    "outputId": "8c9c181d-fbb6-4a8c-b552-07a62d762fdd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.775"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking to see how much % of data still remains\n",
    "(final['Id'].size*1.0)/(filtered_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GqWw3R5rChX6"
   },
   "source": [
    "<b>Observation:-</b> It was also seen that in two rows given below the value of HelpfulnessNumerator is greater than HelpfulnessDenominator which is not practically possible hence these two rows too are removed from calcualtions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D04AzcxlChX-",
    "outputId": "1cedb765-4633-40e8-cd26-5e9ea9112807"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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                        ...                                                                       Text\n",
       "0  64422                        ...                          My son loves spaghetti so I didn't hesitate or...\n",
       "1  44737                        ...                          It was almost a 'love at first bite' - the per...\n",
       "\n",
       "[2 rows x 10 columns]"
      ]
     },
     "execution_count": 84,
     "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": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jgXX4VgCChYG"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ItoTBxEAChYN",
    "outputId": "689f9954-0e4b-4be8-d865-342b6686144c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(87773, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    73592\n",
       "0    14181\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Before starting the next phase of preprocessing lets see the number of entries left\n",
    "print(final.shape)\n",
    "\n",
    "#How many positive and negative reviews are present in our dataset?\n",
    "final['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fu12_ACbChYY"
   },
   "source": [
    "#  [3] Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3pg73rLEChYa"
   },
   "source": [
    "## [3.1].  Preprocessing Review Text\n",
    "\n",
    "Now that we have finished deduplication our data requires some preprocessing before we go on further with analysis and making the prediction model.\n",
    "\n",
    "Hence in the Preprocessing phase we do the following in the order below:-\n",
    "\n",
    "1. Begin by removing the html tags\n",
    "2. Remove any punctuations or limited set of special characters like , or . or # etc.\n",
    "3. Check if the word is made up of english letters and is not alpha-numeric\n",
    "4. Check to see if the length of the word is greater than 2 (as it was researched that there is no adjective in 2-letters)\n",
    "5. Convert the word to lowercase\n",
    "6. Remove Stopwords\n",
    "7. Finally Snowball Stemming the word (it was obsereved to be better than Porter Stemming)<br>\n",
    "\n",
    "After which we collect the words used to describe positive and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BZpKtJnDChYc",
    "outputId": "08059845-51a0-4085-d119-77bb766d535a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n",
      "==================================================\n",
      "The Candy Blocks were a nice visual for the Lego Birthday party but the candy has little taste to it.  Very little of the 2 lbs that I bought were eaten and I threw the rest away.  I would not buy the candy again.\n",
      "==================================================\n",
      "was way to hot for my blood, took a bite and did a jig  lol\n",
      "==================================================\n",
      "My dog LOVES these treats. They tend to have a very strong fish oil smell. So if you are afraid of the fishy smell, don't get it. But I think my dog likes it because of the smell. These treats are really small in size. They are great for training. You can give your dog several of these without worrying about him over eating. Amazon's price was much more reasonable than any other retailer. You can buy a 1 pound bag on Amazon for almost the same price as a 6 ounce bag at other retailers. It's definitely worth it to buy a big bag if your dog eats them a lot.\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "# printing some random reviews\n",
    "sent_0 = final['Text'].values[0]\n",
    "print(sent_0)\n",
    "print(\"=\"*50)\n",
    "\n",
    "sent_1000 = final['Text'].values[1000]\n",
    "print(sent_1000)\n",
    "print(\"=\"*50)\n",
    "\n",
    "sent_1500 = final['Text'].values[1500]\n",
    "print(sent_1500)\n",
    "print(\"=\"*50)\n",
    "\n",
    "sent_4900 = final['Text'].values[4900]\n",
    "print(sent_4900)\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tpufjpl_ChYm",
    "outputId": "261a8bc7-cfd5-4b7f-fa96-959ca8aacd57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n"
     ]
    }
   ],
   "source": [
    "# remove urls from text python: https://stackoverflow.com/a/40823105/4084039\n",
    "sent_0 = re.sub(r\"http\\S+\", \"\", sent_0)\n",
    "sent_1000 = re.sub(r\"http\\S+\", \"\", sent_1000)\n",
    "sent_150 = re.sub(r\"http\\S+\", \"\", sent_1500)\n",
    "sent_4900 = re.sub(r\"http\\S+\", \"\", sent_4900)\n",
    "\n",
    "print(sent_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "giHr0E8zChYv",
    "outputId": "524fc738-3646-486d-b670-e33a2e5d0cd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n",
      "==================================================\n",
      "The Candy Blocks were a nice visual for the Lego Birthday party but the candy has little taste to it.  Very little of the 2 lbs that I bought were eaten and I threw the rest away.  I would not buy the candy again.\n",
      "==================================================\n",
      "was way to hot for my blood, took a bite and did a jig  lol\n",
      "==================================================\n",
      "My dog LOVES these treats. They tend to have a very strong fish oil smell. So if you are afraid of the fishy smell, don't get it. But I think my dog likes it because of the smell. These treats are really small in size. They are great for training. You can give your dog several of these without worrying about him over eating. Amazon's price was much more reasonable than any other retailer. You can buy a 1 pound bag on Amazon for almost the same price as a 6 ounce bag at other retailers. It's definitely worth it to buy a big bag if your dog eats them a lot.\n"
     ]
    }
   ],
   "source": [
    "# https://stackoverflow.com/questions/16206380/python-beautifulsoup-how-to-remove-all-tags-from-an-element\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "soup = BeautifulSoup(sent_0, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)\n",
    "print(\"=\"*50)\n",
    "\n",
    "soup = BeautifulSoup(sent_1000, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)\n",
    "print(\"=\"*50)\n",
    "\n",
    "soup = BeautifulSoup(sent_1500, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)\n",
    "print(\"=\"*50)\n",
    "\n",
    "soup = BeautifulSoup(sent_4900, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HdhtRruPChY3"
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/47091490/4084039\n",
    "import re\n",
    "\n",
    "def decontracted(phrase):\n",
    "    # specific\n",
    "    phrase = re.sub(r\"won't\", \"will not\", phrase)\n",
    "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
    "\n",
    "    # general\n",
    "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
    "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
    "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
    "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
    "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
    "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
    "    return phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S8yht6Y7ChY7",
    "outputId": "716f4785-e4d7-40e4-addb-cf9b065a2f9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "was way to hot for my blood, took a bite and did a jig  lol\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "sent_1500 = decontracted(sent_1500)\n",
    "print(sent_1500)\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zJU8V8P4ChZE",
    "outputId": "0aa7cd16-c67f-41d7-843e-d1166399817a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n"
     ]
    }
   ],
   "source": [
    "#remove words with numbers python: https://stackoverflow.com/a/18082370/4084039\n",
    "sent_0 = re.sub(\"\\S*\\d\\S*\", \"\", sent_0).strip()\n",
    "print(sent_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zUDg3iWhChZL",
    "outputId": "4d26aaf6-d00d-4506-8ba5-514ead41aaa3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "was way to hot for my blood took a bite and did a jig lol\n"
     ]
    }
   ],
   "source": [
    "#remove spacial character: https://stackoverflow.com/a/5843547/4084039\n",
    "sent_1500 = re.sub('[^A-Za-z0-9]+', ' ', sent_1500)\n",
    "print(sent_1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q2QebhvKChZR"
   },
   "outputs": [],
   "source": [
    "# https://gist.github.com/sebleier/554280\n",
    "# we are removing the words from the stop words list: 'no', 'nor', 'not'\n",
    "# <br /><br /> ==> after the above steps, we are getting \"br br\"\n",
    "# we are including them into stop words list\n",
    "# instead of <br /> if we have <br/> these tags would have revmoved in the 1st step\n",
    "\n",
    "stopwords= set(['br', 'the', 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n",
    "            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n",
    "            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n",
    "            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', \\\n",
    "            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n",
    "            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n",
    "            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n",
    "            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n",
    "            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\\\n",
    "            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n",
    "            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n",
    "            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n",
    "            \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\\\n",
    "            \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", \\\n",
    "            'won', \"won't\", 'wouldn', \"wouldn't\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SxOsbLMyChZV",
    "outputId": "3e64146f-e471-4236-8ec2-1f47e066c03e"
   },
   "outputs": [],
   "source": [
    "# Combining all the above stundents \n",
    "from tqdm import tqdm\n",
    "preprocessed_reviews = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in final['Text'].values:\n",
    "    sentance = re.sub(r\"http\\S+\", \"\", sentance)\n",
    "    sentance = BeautifulSoup(sentance, 'lxml').get_text()\n",
    "    sentance = decontracted(sentance)\n",
    "    sentance = re.sub(\"\\S*\\d\\S*\", \"\", sentance).strip()\n",
    "    sentance = re.sub('[^A-Za-z]+', ' ', sentance)\n",
    "    # https://gist.github.com/sebleier/554280\n",
    "    sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in stopwords)\n",
    "    preprocessed_reviews.append(sentance.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HfoRNce9ChZf"
   },
   "source": [
    "<h2><font color='red'>[3.2] Preprocessing Review Summary</font></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CcFUvOHxChZh"
   },
   "outputs": [],
   "source": [
    "## Similartly you can do preprocessing for review summary also."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N317lr0XChZk"
   },
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "54S3lxKpChZl"
   },
   "source": [
    "## [4.1] BAG OF WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OSZKakcCChZm",
    "outputId": "44933996-8b22-4077-f711-94c64099e84e"
   },
   "outputs": [],
   "source": [
    "#BoW\n",
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "count_vect.fit(preprocessed_reviews)\n",
    "print(\"some feature names \", count_vect.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "final_counts = count_vect.transform(preprocessed_reviews)\n",
    "print(\"the type of count vectorizer \",type(final_counts))\n",
    "print(\"the shape of out text BOW vectorizer \",final_counts.get_shape())\n",
    "print(\"the number of unique words \", final_counts.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uCoorSdOChZs"
   },
   "source": [
    "## [4.2] Bi-Grams and n-Grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mXe7GhzAChZt",
    "outputId": "899e8553-b0f1-47a4-f619-530156fb53df"
   },
   "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))\n",
    "# please do read the CountVectorizer documentation http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "\n",
    "# you can choose these numebrs min_df=10, max_features=5000, of your choice\n",
    "count_vect = CountVectorizer(ngram_range=(1,2), min_df=10, max_features=5000)\n",
    "final_bigram_counts = count_vect.fit_transform(preprocessed_reviews)\n",
    "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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iqSqKjvwChZy"
   },
   "source": [
    "## [4.3] TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bNhxI57eChZz",
    "outputId": "f1cee421-33b7-42e2-85cb-eff35527f539"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['aa', 'aafco', 'aback', 'abandon', 'abandoned', 'abdominal', 'ability', 'able', 'abroad', 'absence']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (87773, 11524)\n",
      "the number of unique words including both unigrams and bigrams  11524\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,1),min_df=10)\n",
    "tf_idf_vect.fit(preprocessed_reviews)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "final_tf_idf = tf_idf_vect.transform(preprocessed_reviews)\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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G-sFH6HjChZ4"
   },
   "source": [
    "## [4.4] Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o1F92aCOChZ4"
   },
   "outputs": [],
   "source": [
    "# Train your own Word2Vec model using your own text corpus\n",
    "i=0\n",
    "list_of_sentance=[]\n",
    "for sentance in preprocessed_reviews:\n",
    "    list_of_sentance.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GyjGBihAChZ9",
    "outputId": "5c959683-e87b-4647-ae6a-f0cf168b8017",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model=Word2Vec(list_of_sentance,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aFzlSVQ0ChaC",
    "outputId": "ae0e2fb2-4fda-4ff1-a7a9-064fa294904e"
   },
   "outputs": [],
   "source": [
    "w2v_words = list(w2v_model.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words))\n",
    "print(\"sample words \", w2v_words[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dwDul9PQChaJ"
   },
   "source": [
    "## [4.4.1] Converting text into vectors using Avg W2V, TFIDF-W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HBGVa3eGChaK"
   },
   "source": [
    "#### [4.4.1.1] Avg W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FU2aXSK1ChaL",
    "outputId": "1314ddab-97e5-4996-856e-616f0faad593"
   },
   "outputs": [],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "sent_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in tqdm(list_of_sentance): # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length 50, you might need to change this to 300 if you use google's w2v\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",
    "        if word in w2v_words:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    sent_vectors.append(sent_vec)\n",
    "print(len(sent_vectors))\n",
    "print(len(sent_vectors[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F0b38ZgqChai"
   },
   "source": [
    "#### [4.4.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e9t-BOHYChak"
   },
   "outputs": [],
   "source": [
    "# S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "model = TfidfVectorizer()\n",
    "tf_idf_matrix = model.fit_transform(preprocessed_reviews)\n",
    "# we are converting a dictionary with word as a key, and the idf as a value\n",
    "dictionary = dict(zip(model.get_feature_names(), list(model.idf_)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rt5Z8K0NCha9"
   },
   "source": [
    "# [5] Assignment 11: Truncated SVD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B6Ssh6vvCha9"
   },
   "source": [
    "<ol>\n",
    "    <li><strong>Apply Truncated-SVD on only this feature set:</strong>\n",
    "        <ul>\n",
    "            <li><font color='red'>SET 2:</font>Review text, preprocessed one converted into vectors using (TFIDF)</li>\n",
    "    <br>\n",
    "    <li><strong>Procedure:</strong>\n",
    "        <ul>\n",
    "    <li>Take top 2000 or 3000 features from tf-idf vectorizers using idf_ score.</li>\n",
    "    <li>You need to calculate the co-occurrence matrix with the selected features (Note: X.X^T\n",
    "doesn’t give the co-occurrence matrix, it returns the covariance matrix, check these\n",
    "        bolgs <a href='https://medium.com/data-science-group-iitr/word-embedding-2d05d270b285'>blog-1,</a> <a href='https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/'>blog-2 </a>for more information)</li>\n",
    "            <li>You should choose the n_components in truncated svd, with maximum explained\n",
    "variance. Please search on how to choose that and implement them. (hint: plot of\n",
    "cumulative explained variance ratio)</li>\n",
    "            <li>After you are done with the truncated svd, you can apply K-Means clustering and choose\n",
    "the best number of clusters based on elbow method.</li>\n",
    "            <li> Print out wordclouds for each cluster, similar to that in previous assignment. </li>\n",
    "            <li>You need to write a function that takes a word and returns the most similar words using\n",
    "cosine similarity between the vectors(vector: a row in the matrix after truncatedSVD)</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    \n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9J6ObJ9dCha_"
   },
   "source": [
    "## Truncated-SVD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MkBTVWfDChbA"
   },
   "source": [
    "### [5.1] Taking top features from TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cKI44V1HChbB"
   },
   "outputs": [],
   "source": [
    "idf_score = tf_idf_vect.idf_  # obtaining the idf score from TFIDFVECTORIZER\n",
    "feature_names  = tf_idf_vect.get_feature_names() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "idfscore_feat=[]\n",
    "for i in range(len(idf_score)):\n",
    "    idfscore_feat.append([idf_score[i],feature_names[i]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.98462533540777, 'yucca']\n",
      "[9.98462533540777, 'yougurt']\n",
      "[9.98462533540777, 'yell']\n",
      "[9.98462533540777, 'yeasty']\n",
      "[9.98462533540777, 'yamamotoyama']\n",
      "[9.98462533540777, 'writeup']\n",
      "[9.98462533540777, 'wondeful']\n",
      "[9.98462533540777, 'witch']\n",
      "[9.98462533540777, 'wiser']\n",
      "[9.98462533540777, 'winn']\n"
     ]
    }
   ],
   "source": [
    "idfscore_feat.sort(reverse=True)\n",
    "idfscore_feat=idfscore_feat[:3000]\n",
    "#some top features in idfscore_feat list \n",
    "for i in idfscore_feat[:10]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PiC8j4H4ChbF"
   },
   "source": [
    "### [5.2] Calulation of Co-occurrence matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y0arBDQtChbG"
   },
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#initializing cooccurence matrix to 0 \n",
    "co_occur_matrix=np.zeros((3000,3000))\n",
    "#taking context_window=2\n",
    "context_window=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_3000_features=[]\n",
    "for i in range(3000):\n",
    "    top_3000_features.append(idfscore_feat[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(top_3000_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for sentance in preprocessed_reviews:\n",
    "    words_in_sent=sentance.split()\n",
    "    #print(sentance)\n",
    "    for idx,word in enumerate(words_in_sent):\n",
    "        if word in top_3000_features:\n",
    "            for i in range(max(0,idx-context_window),min(idx+context_window,len(words_in_sent))):\n",
    "                \n",
    "                if words_in_sent[i] in top_3000_features and words_in_sent[i]!=word:\n",
    "                    co_occur_matrix[top_3000_features.index(words_in_sent[i]),top_3000_features.index(word)]+=1\n",
    "                        \n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "#cooccurence matrix\n",
    "print(co_occur_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4kRIoxNBChbJ"
   },
   "source": [
    "### [5.3] Finding optimal value for number of components (n) to be retained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oeWt8Zj0ChbK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_components= 10 Explained variance= 0.28191497905794066\n",
      "n_components= 20 Explained variance= 0.46054455187558463\n",
      "n_components= 50 Explained variance= 0.6995240122244349\n",
      "n_components= 60 Explained variance= 0.7272435278234379\n",
      "n_components= 100 Explained variance= 0.7900139633171888\n",
      "n_components= 200 Explained variance= 0.8459216745627353\n",
      "n_components= 300 Explained variance= 0.8800770181989856\n",
      "n_components= 400 Explained variance= 0.9052384903049173\n",
      "n_components= 500 Explained variance= 0.9251506543654817\n",
      "n_components= 1000 Explained variance= 0.9802733735549959\n",
      "n_components= 1200 Explained variance= 0.9954000409178624\n",
      "n_components= 1500 Explained variance= 1.0000000000000169\n",
      "n_components= 2000 Explained variance= 1.0000000000000102\n"
     ]
    }
   ],
   "source": [
    "# finding optimal value of n_componenets(n) using truncated svd\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "n_components=[10,20,50,60,100,200,300,400,500,1000,1200,1500,2000]\n",
    "explained_variance=[]\n",
    "for n in n_components:\n",
    "    tsvd=TruncatedSVD(n_components=n)\n",
    "    tsvd.fit(co_occur_matrix)\n",
    "    expvar=tsvd.explained_variance_ratio_.sum()\n",
    "    explained_variance.append(expvar)\n",
    "    \n",
    "    print('n_components=',n,'Explained variance=',expvar)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting curve between n_components and explained variance\n",
    "plt.plot(n_components, explained_variance)\n",
    "plt.xlabel('n_components')\n",
    "plt.ylabel(\"Explained variance\")\n",
    "plt.title(\"n_components v/s Explained variance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see that for n_components=1200 it covers explained variance of 99.54% which is great."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CjqXnEtoChbl"
   },
   "source": [
    "### [5.4] Applying k-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tfpn89DQChbn"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "from sklearn.cluster import KMeans\n",
    "#applying K-means clustering and finding best k using elbow method\n",
    "#first put the optimal n_components into truncated svd to find our train data\n",
    "tsvd=TruncatedSVD(n_components=1200)\n",
    "X_train=tsvd.fit_transform(co_occur_matrix)\n",
    "\n",
    "K=[1,2,3,4,5,6,7]\n",
    "inertia=[]\n",
    "\n",
    "for k in K:\n",
    "    model=KMeans(n_clusters=k)\n",
    "    model.fit(X_train)\n",
    "    inertia.append(model.inertia_)\n",
    "plt.plot(K,inertia)\n",
    "plt.xlabel('k (clusters)')\n",
    "plt.ylabel('inertia')\n",
    "plt.title('n_clusters vs inertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "using elbow method we can see that point of inflexion is at k=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training our model at k=3\n",
    "model=KMeans(n_clusters=3).fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U4Gb79QfChbu"
   },
   "source": [
    "### [5.5] Wordclouds of clusters obtained in the above section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mEWn4eD7Chbv"
   },
   "outputs": [],
   "source": [
    "cluster1,cluster2,cluster3=[],[],[]\n",
    "for i in range(model.labels_.shape[0]):\n",
    "    if model.labels_[i] == 0:\n",
    "        cluster1.append(top_3000_features[i])\n",
    "    elif model.labels_[i] == 1:\n",
    "        cluster2.append(top_3000_features[i])\n",
    "    else:\n",
    "        cluster3.append(top_3000_features[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for cluster1\n",
    "data=''\n",
    "for i in cluster1:\n",
    "    data=data+i+' '\n",
    "from wordcloud import WordCloud\n",
    "wordcloud = WordCloud(background_color=\"black\").generate(data)\n",
    "# Display the generated image: \n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for cluster2\n",
    "data=''\n",
    "for i in cluster2:\n",
    "    data=data+i+' '\n",
    "from wordcloud import WordCloud\n",
    "wordcloud = WordCloud(background_color=\"black\").generate(data)\n",
    "# Display the generated image: \n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for cluster3\n",
    "data=''\n",
    "for i in cluster3:\n",
    "    data=data+i+' '\n",
    "from wordcloud import WordCloud\n",
    "wordcloud = WordCloud(background_color=\"black\").generate(data)\n",
    "# Display the generated image: \n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jXT2kiRgChb0"
   },
   "source": [
    "### [5.6] Function that returns most similar words for a given word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IdTy3BRYChb1"
   },
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "def similar_words(word,n):#n=no of similar words want to show\n",
    "    top_words=[]\n",
    "    co_sim=cosine_similarity(co_occur_matrix)#similarity matrix\n",
    "    val=co_sim[top_3000_features.index(word)]\n",
    "    idx=np.argsort(val)#sorting index of top words similar to given word\n",
    "    for i in range(n):\n",
    "        top_words.append(top_3000_features[idx[i]])\n",
    "    return top_words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 words similar to makeup are: \n",
      "['yucca', 'milkbone', 'mex', 'mberry', 'marshmellows', 'marie', 'maraschino', 'manly', 'manipulate', 'lugging']\n"
     ]
    }
   ],
   "source": [
    "#let's find out 10 words similar to word gadget\n",
    "print('Top 10 words similar to makeup are: ')\n",
    "print(similar_words('makeup',10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jRkMCPCyChb6"
   },
   "source": [
    "# [6] Observations and Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) First we figured our top 3000 words using tfidf score built a co-occurence matrix from those words\n",
    "\n",
    "2) After applying truncated svd on it we found our optimal n_components to be 1200 since it covers **99.54%** of explained variance obtained.\n",
    "\n",
    "3) Then we applied kmeans on using optimal svd values and found our best n_cluster value equal to **3** using elbow method.\n",
    "\n",
    "4) After that we plotted wordcloud for each clusters and found in some clusters there are few words and at the end we generated a function which return most similar words using cosine similarity of co-occurence matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LhWddXu6Chb7"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "11 Amazon Fine Food Reviews Analysis_Truncated SVD.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}