Amazon Fine Food Review Analysis.ipynb

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   "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 the Score/Rating. A rating of 4 or 5 could be cosnidered a positive review. A review of 1 or 2 could be considered negative. A review of 3 is nuetral and ignored. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
   ]
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   "source": [
    "## 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 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 id above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."
   ]
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   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\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"
   ]
  },
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   "cell_type": "markdown",
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    "id": "QOI7X2YgCAY_"
   },
   "source": [
    "# [1]. Reading Data"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (5000, 10)\n"
     ]
    },
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       "<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   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",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \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...  "
      ]
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   "source": [
    "\n",
    "# using the SQLite Table to read data.\n",
    "con = sqlite3.connect('database.sqlite') \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 5000\"\"\", con) \n",
    "\n",
    "# Give reviews with Score>3 a positive rating, and reviews with a score<3 a negative rating.\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)"
   ]
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       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Time</th>\n",
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       "      <th>Text</th>\n",
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       "  </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",
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       "      <th>2</th>\n",
       "      <td>#oc-R11DNU2NBKQ23Z</td>\n",
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       "      <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   ProductId             ProfileName        Time  Score  \\\n",
       "0  #oc-R115TNMSPFT9I7  B005ZBZLT4                 Breyton  1331510400      2   \n",
       "1  #oc-R11D9D7SHXIJB9  B005HG9ESG  Louis E. Emory \"hoppy\"  1342396800      5   \n",
       "2  #oc-R11DNU2NBKQ23Z  B005ZBZLT4        Kim Cieszykowski  1348531200      1   \n",
       "3  #oc-R11O5J5ZVQE25C  B005HG9ESG           Penguin Chick  1346889600      5   \n",
       "4  #oc-R12KPBODL2B5ZD  B007OSBEV0   Christopher P. Presta  1348617600      1   \n",
       "\n",
       "                                                Text  COUNT(*)  \n",
       "0  Overall its just OK when considering the price...         2  \n",
       "1  My wife has recurring extreme muscle spasms, u...         3  \n",
       "2  This coffee is horrible and unfortunately not ...         2  \n",
       "3  This will be the bottle that you grab from the...         3  \n",
       "4  I didnt like this coffee. Instead of telling y...         2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "display.head()"
   ]
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     "text": [
      "(80668, 7)\n"
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       "      <td>Breyton</td>\n",
       "      <td>1331510400</td>\n",
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       "      <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",
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       "      <th>2</th>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>#oc-R11O5J5ZVQE25C</td>\n",
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       "      <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   ProductId             ProfileName        Time  Score  \\\n",
       "0  #oc-R115TNMSPFT9I7  B005ZBZLT4                 Breyton  1331510400      2   \n",
       "1  #oc-R11D9D7SHXIJB9  B005HG9ESG  Louis E. Emory \"hoppy\"  1342396800      5   \n",
       "2  #oc-R11DNU2NBKQ23Z  B005ZBZLT4        Kim Cieszykowski  1348531200      1   \n",
       "3  #oc-R11O5J5ZVQE25C  B005HG9ESG           Penguin Chick  1346889600      5   \n",
       "4  #oc-R12KPBODL2B5ZD  B007OSBEV0   Christopher P. Presta  1348617600      1   \n",
       "\n",
       "                                                Text  COUNT(*)  \n",
       "0  Overall its just OK when considering the price...         2  \n",
       "1  My wife has recurring extreme muscle spasms, u...         3  \n",
       "2  This coffee is horrible and unfortunately not ...         2  \n",
       "3  This will be the bottle that you grab from the...         3  \n",
       "4  I didnt like this coffee. Instead of telling y...         2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
  },
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       "              UserId   ProductId                      ProfileName        Time  \\\n",
       "80638  AZY10LLTJ71NX  B001ATMQK2  undertheshrine \"undertheshrine\"  1296691200   \n",
       "\n",
       "       Score                                               Text  COUNT(*)  \n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display[display['UserId']=='AZY10LLTJ71NX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tiaXnkZxCAZO",
    "outputId": "0bbd60e6-e66b-4f20-9060-bf0cfe143b1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "393063"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display['COUNT(*)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TX5b3dc5CAZS"
   },
   "source": [
    "#  Exploratory Data Analysis\n",
    "\n",
    "## [2] 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": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4jW_0WxCCAZT",
    "outputId": "4e526ab2-98f3-46ae-93dc-3e061cb06d59",
    "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   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": 7,
     "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": "BzVTlHiMCAZV"
   },
   "source": [
    "As can be seen above the same user has multiple reviews of the with the 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": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DGy2NM7BCAZX"
   },
   "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": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8b-O13XVCAZZ",
    "outputId": "12b254d4-826d-4cfc-bb6d-455f4d5b2a4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4986, 10)"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S72Yh-rHCAZc",
    "outputId": "146c067f-081c-4e26-f527-71442ff83b8d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.72"
      ]
     },
     "execution_count": 10,
     "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": "LO5nLKeFCAZf"
   },
   "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": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L9z5lrsiCAZh",
    "outputId": "1a0dfa7c-62f4-4469-a97b-38f77fa3b2ab"
   },
   "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   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": 11,
     "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": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8Tphk1piCAZk",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4986, 10)\n"
     ]
    }
   ],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]\n",
    "print(final.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8TpsGDdJCAZm",
    "outputId": "287c6c08-d222-4848-b677-c9a5e769d0b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4986, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    4178\n",
       "0     808\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 13,
     "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": "RxBsuBylCAZr"
   },
   "source": [
    "# [3].  Text Preprocessing.\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": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "laOGMFB7CAZt",
    "outputId": "49d82795-cea4-4695-9834-45978266f6f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why is this $[...] when the same product is available for $[...] here?<br />http://www.amazon.com/VICTOR-FLY-MAGNET-BAIT-REFILL/dp/B00004RBDY<br /><br />The Victor M380 and M502 traps are unreal, of course -- total fly genocide. Pretty stinky, but only right nearby.\n",
      "==================================================\n",
      "I recently tried this flavor/brand and was surprised at how delicious these chips are.  The best thing was that there were a lot of \"brown\" chips in the bsg (my favorite), so I bought some more through amazon and shared with family and friends.  I am a little disappointed that there are not, so far, very many brown chips in these bags, but the flavor is still very good.  I like them better than the yogurt and green onion flavor because they do not seem to be as salty, and the onion flavor is better.  If you haven't eaten Kettle chips before, I recommend that you try a bag before buying bulk.  They are thicker and crunchier than Lays but just as fresh out of the bag.\n",
      "==================================================\n",
      "Wow.  So far, two two-star reviews.  One obviously had no idea what they were ordering; the other wants crispy cookies.  Hey, I'm sorry; but these reviews do nobody any good beyond reminding us to look  before ordering.<br /><br />These are chocolate-oatmeal cookies.  If you don't like that combination, don't order this type of cookie.  I find the combo quite nice, really.  The oatmeal sort of \"calms\" the rich chocolate flavor and gives the cookie sort of a coconut-type consistency.  Now let's also remember that tastes differ; so, I've given my opinion.<br /><br />Then, these are soft, chewy cookies -- as advertised.  They are not \"crispy\" cookies, or the blurb would say \"crispy,\" rather than \"chewy.\"  I happen to like raw cookie dough; however, I don't see where these taste like raw cookie dough.  Both are soft, however, so is this the confusion?  And, yes, they stick together.  Soft cookies tend to do that.  They aren't individually wrapped, which would add to the cost.  Oh yeah, chocolate chip cookies tend to be somewhat sweet.<br /><br />So, if you want something hard and crisp, I suggest Nabiso's Ginger Snaps.  If you want a cookie that's soft, chewy and tastes like a combination of chocolate and oatmeal, give these a try.  I'm here to place my second order.\n",
      "==================================================\n",
      "love to order my coffee on amazon.  easy and shows up quickly.<br />This k cup is great coffee.  dcaf is very good as well\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": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MvD0JaQ9CAZx",
    "outputId": "dcc683d4-6014-4430-fb91-f9526bca4475"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why is this $[...] when the same product is available for $[...] here?<br /> /><br />The Victor M380 and M502 traps are unreal, of course -- total fly genocide. Pretty stinky, but only right nearby.\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",
    "print(sent_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Liu2zNFLCAZ0",
    "outputId": "f66770ef-17d3-4a99-df7f-75242858701d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why is this $[...] when the same product is available for $[...] here? />The Victor M380 and M502 traps are unreal, of course -- total fly genocide. Pretty stinky, but only right nearby.\n",
      "I recently tried this flavor/brand and was surprised at how delicious these chips are.  The best thing was that there were a lot of \"brown\" chips in the bsg (my favorite), so I bought some more through amazon and shared with family and friends.  I am a little disappointed that there are not, so far, very many brown chips in these bags, but the flavor is still very good.  I like them better than the yogurt and green onion flavor because they do not seem to be as salty, and the onion flavor is better.  If you haven't eaten Kettle chips before, I recommend that you try a bag before buying bulk.  They are thicker and crunchier than Lays but just as fresh out of the bag.\n",
      "==================================================\n",
      "Wow.  So far, two two-star reviews.  One obviously had no idea what they were ordering; the other wants crispy cookies.  Hey, I'm sorry; but these reviews do nobody any good beyond reminding us to look  before ordering.These are chocolate-oatmeal cookies.  If you don't like that combination, don't order this type of cookie.  I find the combo quite nice, really.  The oatmeal sort of \"calms\" the rich chocolate flavor and gives the cookie sort of a coconut-type consistency.  Now let's also remember that tastes differ; so, I've given my opinion.Then, these are soft, chewy cookies -- as advertised.  They are not \"crispy\" cookies, or the blurb would say \"crispy,\" rather than \"chewy.\"  I happen to like raw cookie dough; however, I don't see where these taste like raw cookie dough.  Both are soft, however, so is this the confusion?  And, yes, they stick together.  Soft cookies tend to do that.  They aren't individually wrapped, which would add to the cost.  Oh yeah, chocolate chip cookies tend to be somewhat sweet.So, if you want something hard and crisp, I suggest Nabiso's Ginger Snaps.  If you want a cookie that's soft, chewy and tastes like a combination of chocolate and oatmeal, give these a try.  I'm here to place my second order.\n",
      "==================================================\n",
      "love to order my coffee on amazon.  easy and shows up quickly.This k cup is great coffee.  dcaf is very good as well\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",
    "\n",
    "text = soup.get_text()\n",
    "print(text)\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": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lNHWbzBaCAZ3"
   },
   "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": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y7v1fmngCAZ5",
    "outputId": "22e3fcb6-c2e6-4c92-be48-f65543b1140f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wow.  So far, two two-star reviews.  One obviously had no idea what they were ordering; the other wants crispy cookies.  Hey, I am sorry; but these reviews do nobody any good beyond reminding us to look  before ordering.<br /><br />These are chocolate-oatmeal cookies.  If you do not like that combination, do not order this type of cookie.  I find the combo quite nice, really.  The oatmeal sort of \"calms\" the rich chocolate flavor and gives the cookie sort of a coconut-type consistency.  Now let is also remember that tastes differ; so, I have given my opinion.<br /><br />Then, these are soft, chewy cookies -- as advertised.  They are not \"crispy\" cookies, or the blurb would say \"crispy,\" rather than \"chewy.\"  I happen to like raw cookie dough; however, I do not see where these taste like raw cookie dough.  Both are soft, however, so is this the confusion?  And, yes, they stick together.  Soft cookies tend to do that.  They are not individually wrapped, which would add to the cost.  Oh yeah, chocolate chip cookies tend to be somewhat sweet.<br /><br />So, if you want something hard and crisp, I suggest Nabiso is Ginger Snaps.  If you want a cookie that is soft, chewy and tastes like a combination of chocolate and oatmeal, give these a try.  I am here to place my second order.\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "sent_1500 = decontracted(sent_1500)\n",
    "print(sent_1500)\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jKIBLrAjCAZ7",
    "outputId": "376f1e55-511a-4352-c345-a39f1d2a2224"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why is this $[...] when the same product is available for $[...] here?<br /> /><br />The Victor  and  traps are unreal, of course -- total fly genocide. Pretty stinky, but only right nearby.\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": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sU3eY6geCAZ9",
    "outputId": "90d91c93-0d77-47c0-e496-77d1611a86fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wow So far two two star reviews One obviously had no idea what they were ordering the other wants crispy cookies Hey I am sorry but these reviews do nobody any good beyond reminding us to look before ordering br br These are chocolate oatmeal cookies If you do not like that combination do not order this type of cookie I find the combo quite nice really The oatmeal sort of calms the rich chocolate flavor and gives the cookie sort of a coconut type consistency Now let is also remember that tastes differ so I have given my opinion br br Then these are soft chewy cookies as advertised They are not crispy cookies or the blurb would say crispy rather than chewy I happen to like raw cookie dough however I do not see where these taste like raw cookie dough Both are soft however so is this the confusion And yes they stick together Soft cookies tend to do that They are not individually wrapped which would add to the cost Oh yeah chocolate chip cookies tend to be somewhat sweet br br So if you want something hard and crisp I suggest Nabiso is Ginger Snaps If you want a cookie that is soft chewy and tastes like a combination of chocolate and oatmeal give these a try I am here to place my second order \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": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xsr4xHkJCAaA"
   },
   "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",
    "stop_words= 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\"])\n",
    "# from nltk.corpus import stopwords\n",
    "# #nltk.download('stopwords')\n",
    "# stop_words=set(stopwords.words('english'))\n",
    "# print(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MgsJ09NpCAaB",
    "outputId": "1d7ef7e7-d03e-4bff-850a-a2aef70af7d9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 4986/4986 [00:01<00:00, 2629.75it/s]\n"
     ]
    }
   ],
   "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 tqdm(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 stop_words)\n",
    "    preprocessed_reviews.append(sentance.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f_zfwXXWCAaE",
    "outputId": "a8ba4cbf-3a4c-4447-f06a-2b639e1a5f88",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wow far two two star reviews one obviously no idea ordering wants crispy cookies hey sorry reviews nobody good beyond reminding us look ordering chocolate oatmeal cookies not like combination not order type cookie find combo quite nice really oatmeal sort calms rich chocolate flavor gives cookie sort coconut type consistency let also remember tastes differ given opinion soft chewy cookies advertised not crispy cookies blurb would say crispy rather chewy happen like raw cookie dough however not see taste like raw cookie dough soft however confusion yes stick together soft cookies tend not individually wrapped would add cost oh yeah chocolate chip cookies tend somewhat sweet want something hard crisp suggest nabiso ginger snaps want cookie soft chewy tastes like combination chocolate oatmeal give try place second order'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_reviews[1500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tmeU-LoVCAaI"
   },
   "source": [
    "<h2><font color='red'>[3.2] Preprocess Summary</font></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Case 1:  thirty bucks? \n",
      "Case 2:  Best sour cream & onion chip I've had\n"
     ]
    }
   ],
   "source": [
    "# Let's take critical cases of two Summaries where we need to optimise: (say index 0 & 1000)\n",
    "\n",
    "print(\"Case 1: \",final['Summary'].values[0], \"\\nCase 2: \" ,final['Summary'].values[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wtJ2hcy9CAaI"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 4986/4986 [00:01<00:00, 3666.05it/s]\n"
     ]
    }
   ],
   "source": [
    "## Similartly you can do preprocessing for review summary also.\n",
    "## Using the functions defined for text we can preprocess Summary section too...\n",
    "## \n",
    "\n",
    "from tqdm import tqdm\n",
    "preprocessedSummary = []\n",
    "#tqdm is for printing the status bar\n",
    "for sentance in tqdm(final['Summary'].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 stop_words)\n",
    "    preprocessedSummary.append(sentance.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preprosessed summary 1:  thirty bucks \n",
      "preprocessed summary 2:  best sour cream onion chip\n"
     ]
    }
   ],
   "source": [
    "# It's the processed string for above cases: \n",
    "\n",
    "print(\"preprosessed summary 1: \", preprocessedSummary[0], \"\\npreprocessed summary 2: \", preprocessedSummary[1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ytBZVEs2CAaL"
   },
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9amw6tMZCAaL"
   },
   "source": [
    "## [4.1] BAG OF WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3hasoJ-_CAaN",
    "outputId": "12aa3f3f-083c-4de4-a6e7-3dbde6b5a905",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aahhhs', 'aback', 'abandon', 'abates', 'abbott', 'abby', 'abdominal', 'abiding', 'ability']\n",
      "=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:=:\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (4986, 12997)\n",
      "the number of unique words  12997\n",
      "[[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": [
    "#BoW\n",
    "# print(count_vect.get_feature_names()[:10].index('product'))\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])\n",
    "print((final_counts.toarray()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZraqlfzfAo40s8vC5d1m9rHOz5KeBP5qZo9LugtYbmZ/aKss3kbi4qGiuo6fP/ceTywqZtTg/lw/8xBKq2p4aXUpCzaUUdfQRHZ6Kp+cmM+pYWDp7uqgSJvK9jB/7Q7mrynljfVl7K5tIEUwZVQuJ04s4KSJ+UwZldtjrzpc9+sJbSTTgXVmtiEs0OPAuUDUQALMAm5qa4MKfl6cClwUJj0A3Ay0GUic60pmxpzlm/nJs6sor67nypPH851PTaJ/etBT6NITxlFd18Br68p46b2gd9PclduAfQ3UMw4eypTCrm2gNjMq9zawraqGrbtq2FYZPIp37uWNDWUU7awGYGRuf86eMoKTJuZz/IR8BmX1jC6krveKZyAZCRRHLJcAx0bLKGkMMA54KSI5U9IioAG41cyeBoYAFWbWELHNka1s8wrgCoDRo0d34jBcb1dUVs2KDyo4fkI+gzvZhbOorJofPfMO89eUMmVULg9edjiTR+R8LF9WehqnTR7GaZOHYWa8t7WKl97bzsvvbefOl9dxx0vrGJydztFj8shOTyUjLfXDYS4y0lLJ7JfysbSMtGAojN21jWxrDhRVtcHzqmA5skdSs9ysfkwbM5jLTxz3YWOzV/m4rhTPQBLtm9paPdqFwGwza4xIG21mmyWNB16S9DZQGes2zexu4G4IqrZiL7brS15ft4MrH15MVU0DqSli+tjBzDzsAE4/dBjDB/WPeTv1jU3c8+pGbv/XGlIlbj57Ml/5xNiYrigkccjwHA4ZnsM3ZxxI+Z664Ca+97azaktlcIdyfXA3cm1DEzX1jR+7QS+azH4pHBDeqDelMPfDG/eGRdwhPiwnM+7jLDkXz0BSAoyKWC4ENreS90Lgm5EJZrY5/LtB0ivAkcBfgFxJaeFVSVvbdEnuz4uKueGvbzO+IJvfXzyZtzbu5Pl3tnLTnJXcNGclU0blcsahw5h56AGML2h9LKJlxRVc/5cVvLe1itMnD+PH5x7aoSDUUl52OudOHcm5U6NeTGNmNDRZGGAaw6EwgkBTUx+0uwzNySQnM82vLFyPEM/G9jSCxvZPAR8QNLZfZGYrW+Q7CJgLjLOwMJLygGozq5WUD7wBnGtmqyT9GfhLRGP7CjP7fVtl8cb25GJm/N+La7jjpXV88sB8fv/lo8jJ3NcOsL50N3NXbmXuO1tZXrILgIlDBzDzsAM449ADOHREDpKoqqnnV3NX8+CCTQwbmMnN5xzKzMMOSNRhOdftYm1sj+ud7ZI+A/yGoPvvvWb2M0m3AIvMbE6Y52Yg08yuj1jveOCPQBPB5Fu/MbM/ha+NZ1/336XAl82stq1yeCBJHrUNjXx/9gqeWbaZL00bxU8/d1ibvZA2V+zlhZVbeX7lVt7auJMmCxqjZxxcwD9XbWdbVQ1fPW4M3zvjIAZmeqO0Sy49IpD0FB5IkkP5njqufGgxb72/k+vOOIj/PGVCh6p+ynbX8q93tzN35VZeXbuD8QXZ/Pzzh3Pk6Lw4ltq5nqsndP91rtu8v2MPl92/kJKKvfx21pGcM2VEh7cxZEAGFxwziguOGUVdQxP9UuVtEM7FwAOJ6/UWb9rJ1x9cjJnx6OXHMm3s4E5vsyfeje5cT+WBxPVqz67YzLVPLmdkbn/uvfQYxvXCca6c6+08kLheycy4a94G/vf59zhmbB53f2WazxfhXIJ4IHG9Tn1jEzc+8w6PvVXMOVNG8Ivzj/Cb7pxLIA8krlfZVlnD9/68nFfX7uDqGQdy7WmTSInzhErOubZ5IHG9QmVNPX+ct54//XsjjU3GL75wBBccM6r9FZ1zceeBxPVotQ2NPLygiN+9tJby6nrOmTKC/zp9EmOGeKO6cz2FBxLXIzU1BUO1/+qF1ZSU7+WTB+Zz/ZkHf2wGPOdc4nkgcT2KmTF/7Q5u/cd7vLulkkNH5PDzzx/OiRML2l/ZOZcQHkhcj/F2yS5uff5dXltXRmFef26/cCpnHzHCG9Od6+E8kLj9ZmaUlO9lRckuVnxQwdslu9i5p46CgRkUDMigICf4OzQnM/ybQcHADAZmfHT4801le/jl3NU8u2ILg7PTuensyVx07Ggy0rxLr3O9gQcSFxMzY1tlLctLgoCx4oNdvF1SQXl1PQD9UoPJmwrz+rNjdx0bSvdQWlVLXePHZ+zL7JdCwcAMhg7MZGBmGv9eu4N+qSl869QDueKk8T7KrnO9jAcSF5WZ8dq6MhZvKmdFSQUrPthFaVUwWn9qipg0bCCnTz6AwwsHMaUwl0kHDPjYFYSZsWtvPaVVtWyvqg3/1nxkeXPFXi44ZhTf+dREhuZkJuJQnXOd5IHERfXQgk3c+MxKJJhQMIATJ+ZzxMhBHF6Yy+ThOfRPb7/aSRK5WenkZqUzcdjAbii1cy4RPJC4jynfU8evX1jD8ROGcPdXpzEgw78mzrnWxXWsbEkzJa2WtE7S9VFev03SsvCxRlJFmD5V0huSVkpaIelLEevcL2ljxHpT43kMyeg3/1xDVU09N519qAcR51y74naWkJQK3AmcBpQACyXNMbNVzXnM7LsR+b8FHBkuVgNfNbO1kkYAiyXNNbOK8PXrzGx2vMqezFZvreLhN4v48nFjOOgAr45yzrUvnlck04F1ZrbBzOoI5lk/t438s4DHAMxsjZmtDZ9vBrYDfkdanJkZtzy7kgEZaXz305MSXRznXC8Rz0AyEiiOWC4J0z5G0hhgHPBSlNemA+nA+ojkn4VVXrdJyui6Iie3F1dt47V1ZVx72iSf28M5F7N4BpJotyNbK3kvBGabWeNHNiANBx4CvmZmzTck3AAcDBwDDAZ+EHXn0hWSFklaVFpauj/lTyq1DY389O/vMnHoAC4+dnSii+Oc60XiGUhKgMhxvguBza3kvZCwWquZpBzg78CPzGxBc7qZbbFALXAfQRXax5jZ3WY2zcymFRR4rVh77v33+xTtrObGsyeTlurzlTvnYhfPM8ZCYKKkcZLSCYLFnJaZJB0E5AFvRKSlA08BD5rZn1vkHx7+FXAe8E7cjiBJbK+s4XcvreXThwzzwRGdcx0Wt15bZtYg6WpgLpAK3GtmKyXdAiwys+agMgt43Mwiq70uAE4Chki6NEy71MyWAY9IKiCoOlsGXBWvY0gWv5y7mrrGJn702UMSXRTnXC8U15sEzOw54LkWaTe2WL45ynoPAw+3ss1Tu7CISW95cQV/XlzClSePZ2y+TxblnOs4rwxPYmbGj/+2kvwBGVw948BEF8c510t5IElic5ZvZklRBd8/4yAfcdc5t988kCSp6roGfv7cexw+chDnH12Y6OI453oxDyRJ6q5X1rO1soabzp7sMxA65zrFA0kSKimv5o/zN3DOlBFMGzs40cVxzvVyHkiS0M//8R4SXH/mwYkuinOuD/BAkmTe3FDG31ds4aqTJzAit3+ii+Oc6wM8kCSRxibjx39bxYhBmVx50oREF8c510d4IEkiTy4qZtWWSm74zCExTZXrnHOx8ECSJHbtredXc1dzzNg8zjpieKKL45zrQ3we1SRxx7/WsrO6jgfOnk4w3qVzznUNvyJJArtrG3jwjU2cf1Qhh40clOjiOOf6GA8kSeD1dTuoa2zic0dFnaDSOec6xQNJEpi3ppSs9FSmjfGbD51zXc8DSR9nZsxbU8rxE/JJT/OP2znX9fzM0sdt2LGHkvK9nHyQz3zonIsPDyR93LzVpQCc7FPoOufiJK6BRNJMSaslrZN0fZTXb5O0LHyskVQR8dolktaGj0si0o+W9Ha4zd/K+7K2ad6aUsbnZzN6SFaii+Kc66PiFkgkpQJ3AmcCk4FZkiZH5jGz75rZVDObCtwB/DVcdzBwE3AsMB24SVJeuNofgCuAieFjZryOoberqW9kwYYyTprkVyPOufiJ5xXJdGCdmW0wszrgceDcNvLPAh4Ln58BvGhmO82sHHgRmClpOJBjZm+YmQEPAufF7xB6tzc37qS2ocnbR5xzcRXPQDISKI5YLgnTPkbSGGAc8FI7644Mn8eyzSskLZK0qLS0dL8OoLebt7qU9LQUjhs3JNFFcc71YfEMJNHaLqyVvBcCs82ssZ11Y96mmd1tZtPMbFpBQXL+Ip+3ZjvHjhvsAzQ65+IqnoGkBBgVsVwIbG4l74Xsq9Zqa92S8Hks20xqxTurWV+6h5O9fcQ5F2fxDCQLgYmSxklKJwgWc1pmknQQkAe8EZE8FzhdUl7YyH46MNfMtgBVko4Le2t9FXgmjsfQa81fG1TnneLtI865OIs5kEjK7siGzawBuJogKLwLPGlmKyXdIumciKyzgMfDxvPmdXcCPyEIRguBW8I0gG8A9wDrgPXAPzpSrmQxb3UpI3P7M6FgQKKL4pzr49odRl7S8QQn7gHAaElTgCvN7D/bW9fMngOea5F2Y4vlm1tZ917g3ijpi4DD2tt3MqtraOL19WWcPWWEDxnvnIu7WK5IbiPojlsGYGbLgZPiWSjXOUuKytld2+DtI865bhFT1ZaZFbdIaoya0fUI89aUkpYijj/Qu/065+IvlhkSi8PqLQsbza8haPNwPdS81aUcNSaPnMx+iS6Kcy4JxHJFchXwTfbdDDg1XHY90PbKGlZtqfRqLedct2nziiQcL+srZnZxN5XHddL8tTsAPJA457pNm1ck4Z3mbY2P5XqYeWtKyR+QweThOYkuinMuScTSRvKapN8BTwB7mhPNbEncSuX2S2OT8eraUk49eCgpKd7t1znXPWIJJMeHf2+JSDPg1K4vjuuMFSUVVFTXe7WWc65btRtIzGxGdxTEdd68NaVIcKLPhuic60bt9tqSNEjS/zUPyS7p15IGdUfhXMfMW1PKEYW5DM5OT3RRnHNJJJbuv/cCVcAF4aMSuC+ehXIdV76njuXFFZzi1VrOuW4WSxvJBDP7QsTyjyUti1eB3P7597odNBk+G6JzrtvFckWyV9InmxcknQDsjV+R3P6Yt6aUQf37MaUwN9FFcc4lmViuSL4BPBDRLlIOXBq3ErkOMzPmrSnlxIn5pHq3X+dcN4ul19YyYIqknHC5Mu6lch3y7pYqSqtqvduvcy4hYum19T+Scs2s0swqw1kLf9odhXOxmbcmmA3RA4lzLhFiaSM508wqmhfMrBz4TCwblzRT0mpJ6yRd30qeCyStkrRS0qNh2gxJyyIeNZLOC1+7X9LGiNemxlKWvmzemu0cMjyHoTmZiS6Kcy4JxdJGkiopw8xqAST1BzLaWykc8PFO4DSCUYMXSppjZqsi8kwEbgBOMLNySUMBzOxlglGGkTSYYFrdFyI2f52ZzY7lAPu63bUNLHq/nMtPHJ/oojjnklQsgeRh4F+S7iMYGuUy4IEY1psOrDOzDQCSHicYAHJVRJ6vA3eGVzmY2fYo2zkf+IeZVcewz6Tz+rodNDSZV2s55xKm3aotM/sF8FPgEOBQ4CdhWntGApEzK5aEaZEmAZMkvSZpgaSZUbZzIfBYi7SfSVoh6TZJUa+OJF3RfDd+aWlpDMXtneatKSU7PZWjx+QluijOuSQVS2N7NvCCmX0PuBvIkBTL1HvR+qFai+U0YCJwCjALuEfShzdCSBoOHA7MjVjnBuBg4BhgMPCDaDs3s7vNbJqZTSso6Ju/1pu7/R5/YD7paTHNmuycc10ulrPPfCBT0kjgn8DXgPtjWK8EGBWxXAhsjpLnGTOrN7ONwGqCwNLsAuApM6tvTjCzLRaoJRiqZXoMZemTNuzYQ0n5Xq/Wcs4lVCyBRGH7xOeBO8zsc8DkGNZbCEyUNC6c6/1CYE6LPE8DMwAk5RNUdW2IeH0WLaq1wqsUJAk4D3gnhrL0SfNWe7df51zixdLYLkmfAC4G/iPW9cysQdLVBNVSqcC9ZrZS0i3AIjObE752uqRVQCNBb6yycKdjCa5o5rXY9COSCgiqzpYRzCmflOatKWV8QTajBmcluijOuSQWSyD5NkG7xFNhIBgPvBzLxs3sOeC5Fmk3Rjw34Nrw0XLd9/l44zxm5hNqATX1jSzYUMZFx45OdFGcc0kuliuL+QTtJM3LG4Brmpcl3WFm34pP8Vxr3ty4k9qGJq/Wcs4lXFd09TmhC7bhOmje6lIy0lI4bvyQRBfFOZfkvM93hzvtAAAX5klEQVRoLzVvzXaOHT+EzH6piS6Kcy7JeSDphYp3VrO+dI9XaznneoSuCCQ+AUY3m7/Wu/0653qOrggkt3fBNlwHzF25jZG5/ZlQkJ3oojjnXExDpLzYYtiSPEkfDlliZvfHqWwuiuKd1by6tpQvHF1IcE+mc84lVixXJPlR5iMZGr8iubY89lYRAi48ZlS7eZ1zrjvEEkiaJH1415ukMXx88EXXDeoamnhyUTGnHjyMEbn9E10c55wDYruz/b+Bf0tqHqrkJOCK+BXJteaFVVvZsbuOi4/zu9mdcz1HLHe2Py/pKOA4gh5a3zWzHXEvmfuYRxYUUZjXn5Mmem8t51zPEUtj+0kEo/1WAruAyWGa60brtu/mjQ1lzJo+mtQUb2R3zvUcsVRtXRfxPJNg/o/FgA+e2I0ee6uItBRxwTRvZHfO9SyxVG2dHbksaRQQy1S7rovU1Dcye3EJZxx2AAUDo84s7JxzCbM/NySWAId1dUFc6/6+Ygu79tZzsQ8Z75zrgdq9IpF0B/u6+6YAU4Hl8SyU+6hH3tzE+PxsPuEj/TrneqBYrkgWEbSJLAbeAH5gZl+OZeOSZkpaLWmdpOtbyXOBpFWSVkp6NCK9UdKy8DEnIn2cpDclrZX0RDiNb5+1anMlS4oquOjY0X4nu3OuR4qljeSB/dmwpFTgTuA0guqwhZLmmNmqiDwTCWZfPMHMyiVF3jG/18ymRtn0/wK3mdnjku4imP73D/tTxt7g0bc2kZ6WwvlHFya6KM45F1Us3X8nSpodXjVsaH7EsO3pwDoz22BmdcDjwLkt8nwduDMcdgUz295OWUTQW2x2mPQAcF4MZemV9tQ28PTSzZx1xHBys/r0hZdzrheLpWrrPoJf/A3ADOBB4KEY1hsJFEcsl/DxOdgnAZMkvSZpgaSZEa9lSloUpjcHiyFAhZk1tLFNACRdEa6/qLS0NIbi9jxzlm9md20DFx87JtFFcc65VsUSSPqb2b8AmdkmM7uZ2O4hiVah33KMrjRgInAKMAu4J2Kk4dFmNg24CPiNpAkxbjNINLvbzKaZ2bSCgt53J7iZ8fCCTRx8wECOGp3b/grOOZcgsQSSGkkpwFpJV0v6HLGN/lsCRN49VwhsjpLnGTOrN7ONwGqCwIKZbQ7/bgBeAY4EdgC5ktLa2GafsKJkFys3V3KxN7I753q4WALJd4As4BrgaODLwCUxrLcQmBj2skoHLgTmtMjzNEF1GZLyCaq6NoRznmREpJ8ArDIzA14Gzg/XvwR4Joay9DqPvLmJrPRUzjsyas2dc871GO0GEjNbaGa7zazEzL5mZl8wswXNr4f3mURbrwG4GpgLvAs8aWYrJd0i6Zww21ygTNIqggBxnZmVAYcAiyQtD9Nvjejt9QPgWknrCNpM/rQ/B96T7dpbz5zlmzl36ggGZvZLdHGcc65NsYy11Z4TWnvBzJ4DnmuRdmPEcwOuDR+ReV4HDm9lmxsIeoT1WU8tKaGmvomLpnsju3Ou5+uKOdtdFzIzHnmziCmFgzi8cFCii+Occ+3yQNLDLHy/nLXbd3uXX+dcr9EVgcS7FHWhR97cxMDMNM6aMjzRRXHOuZh0RSC5vQu24YCy3bX84+2tfOGoQrLSu6L5yjnn4i+WIVJejLhJkLBr7tzmZTO7P05lSzqzF5dQ19jERT5cvHOuF4nliiTfzCqaF8JxsWK5IdF1QFOT8ehbRUwfO5hJwwYmujjOORezWAJJk6QPfyJLGkMrw5K4/ffa+h1sKqvm4uP8asQ517vEUhH/38C/Jc0Ll08CrohfkZLTIwuKGJydzszDDkh0UZxzrkNimY/keUlHAceFSd81sx3xLVZy2VZZw4vvbuPyT44jIy010cVxzrkOibVr0PEEVyLNno1DWZLWEwuLaWwyZk33ai3nXO8TS6+tW4FvA6vCx7cl/TzeBUsWjU3G428VceLEfMbmZye6OM4512GxXJF8BphqZk0Akh4AlhJMkes6wcy48+V1bN5Vw41nT050cZxzbr/EWrWVC+wMn/sAUF2gpr6RHz71Nn9d8gGfPWI4nz5kWKKL5Jxz+6XNQBLOkf4rYKmklwmGQzkJvxrplO1VNVz10GKWFFVw7WmT+NapB/rkVc65XqvNQGJmJunbBD22jiEIJD8ws63dUbi+6J0PdnHFg4vYWV3H7y8+is8c7mNqOed6t1iqthYAhWbWcnZD10HPv7OF7z6xnNysfsy+6ngOG+m1hM653i+WQDIDuFLSJmAPwVWJmdkRcS1ZH2Jm/O6ldfz6xTVMHZXL3V85mqE5mYkulnPOdYlYAsmZ+7txSTMJRgdOBe4xs1uj5LkAuJlg2JXlZnaRpKnAH4AcoBH4mZk9Eea/HzgZ2BVu4lIzW7a/ZYy3mvpGrpu9gr8t38x5U0dw6xeOILOf33TonOs7YrmzfdP+bFhSKnAncBpQAiyUNCdi7nUkTSRouD/BzMolNQ8GWQ181czWShoBLJY0N2LwyOvMbPb+lKs7baus4esPLuLtD3bx/ZkH8Y2TJ3ijunOuz4nnpBfTgXXhHOtIehw4l+CmxmZfB+4MRxTGzLaHf9c0ZzCzzZK2AwVABb3E8uIKrnhoEVU1Dfzxy0dz+qE+hpZzrm+K51S7I4HiiOWSMC3SJGCSpNckLQirwj5C0nQgHVgfkfwzSSsk3SYpI9rOJV0haZGkRaWlpZ07kg762/LNXPDHN0hLSeEv3zjeg4hzrk+LZyCJVofTcvj5NGAicAowC7inxSRaw4GHgK8131lPUBV2MEF35MHAD6Lt3MzuNrNpZjatoKCgM8fRIXf8ay3femwpRxQO4pmrT+CQ4Tndtm/nnEuEeAaSEmBUxHIhsDlKnmfMrN7MNgKrCQILknKAvwM/MrMFzSuY2RYL1AL3EVSh9Qjbq2r49Ytr+Ozhw3n48mPJHxD1Ysk55/qUeAaShcBESeMkpQMXAi3vRXmaoHsxkvIJqro2hPmfAh40sz9HrhBepTTfdX8e8E4cj6FDlhYFTTiXfXKsDwfvnEsacWtsN7MGSVcDcwm6/95rZisl3QIsCm9wnAucLmkVQTff68ysTNKXCYZiGSLp0nCTzd18H5FUQFB1tgy4Kl7H0FFLiyrolyoOHeE3GjrnkofM+v6sudOmTbNFixbFfT8X/PENahuaeOabJ8R9X845F2+SFpvZtPbyxbNqK6k0NDaxoqSCI0fltp/ZOef6EA8kXeS9rVXU1Ddx1Ji8RBfFOee6lQeSLrK0qBzAr0icc0nHA0kXWVpUQf6ADArz+ie6KM451608kHSRpcUVHDU618fScs4lHQ8kXWDnnjo27tjDkaO9fcQ5l3w8kHSBZcVB+8hRo719xDmXfDyQdIGlRRWkpojDC/1GROdc8vFA0gWWFJVz8AEDyUqP56j8zjnXM3kg6aTGJmN58S6O8vYR51yS8kDSSWu3V7G7toEjvX3EOZekPJB0UvOIv95jyzmXrDyQdNLSonLysvoxdkhWoovinHMJ4YGkk5YUVXDk6Dy/EdE5l7Q8kHTCrr31rNu+28fXcs4lNQ8knbC8OGgf8RF/nXPJLK6BRNJMSaslrZN0fSt5LpC0StJKSY9GpF8iaW34uCQi/WhJb4fb/K0SWKe0pKgcCY7wGxGdc0ksbnfQSUoF7gROA0qAhZLmmNmqiDwTgRuAE8ysXNLQMH0wcBMwDTBgcbhuOfAH4ApgAfAcMBP4R7yOoy1LiyqYNHQgAzP7JWL3zjnXI8TzimQ6sM7MNphZHfA4cG6LPF8H7gwDBGa2PUw/A3jRzHaGr70IzJQ0HMgxszcsmCP4QeC8OB5Dq5qajGXFFRw1xttHnHPJLZ6BZCRQHLFcEqZFmgRMkvSapAWSZraz7sjweVvbBEDSFZIWSVpUWlraicOIbsOOPezaW8+Ro7x9xDmX3OIZSKK1XViL5TRgInAKMAu4R1JuG+vGss0g0exuM5tmZtMKCgpiLnSsPpwR0e9od84luXgGkhJgVMRyIbA5Sp5nzKzezDYCqwkCS2vrloTP29pmt1haXMHAzDQmFAxIxO6dc67HiGcgWQhMlDROUjpwITCnRZ6ngRkAkvIJqro2AHOB0yXlScoDTgfmmtkWoErScWFvra8Cz8TxGFq1ZFM5U0flkpLiNyI655Jb3HptmVmDpKsJgkIqcK+ZrZR0C7DIzOawL2CsAhqB68ysDEDSTwiCEcAtZrYzfP4N4H6gP0FvrW7vsbW7toE126o4/dADunvXzjnX48R1Ag0ze46gi25k2o0Rzw24Nny0XPde4N4o6YuAw7q8sB2woqSCJvMZEZ1zDvzO9v3SPOLvVB8axTnnPJDsj6VF5YwvyCY3Kz3RRXHOuYTzQNJBZsbSogqfEdE550IeSDqoaGc1ZXvq/P4R55wLeSDpoA9nRPQ72p1zDvBA0mFLi8rJSk/loAMGJroozjnXI3gg6aAlRRVMKcwl1W9EdM45wANJh+yta+TdLZXePuKccxE8kHTAO5t30dBk3mPLOecieCDpgCWbghF/p/oViXPOfcgDSQcsLapg9OAs8gdkJLoozjnXY3ggiZGZsaSo3MfXcs65FjyQxGjzrhq2V9VypLePOOfcR3ggiZHPiOicc9F5IInR0qIKMtJSOGR4TqKL4pxzPYoHkhgtKSrniMJB9Ev1t8w55yLF9awoaaak1ZLWSbo+yuuXSiqVtCx8XB6mz4hIWyapRtJ54Wv3S9oY8drUeB4DQG1DIys/qPT2EeeciyJuMyRKSgXuBE4DSoCFkuaY2aoWWZ8ws6sjE8zsZWBquJ3BwDrghYgs15nZ7HiVvaVVmyupa2zyHlvOORdFPK9IpgPrzGyDmdUBjwPn7sd2zgf+YWbVXVq6DljSPOKvX5E459zHxDOQjASKI5ZLwrSWviBphaTZkkZFef1C4LEWaT8L17lNUtS7AyVdIWmRpEWlpaX7dQDNlhaVMzK3P8NyMju1Heec64viGUiiDY9rLZb/Bow1syOAfwIPfGQD0nDgcGBuRPINwMHAMcBg4AfRdm5md5vZNDObVlBQsH9HEFpaVOHDojjnXCviGUhKgMgrjEJgc2QGMyszs9pw8f8Djm6xjQuAp8ysPmKdLRaoBe4jqEKLm+2VNXxQsZcjR3kgcc65aOIZSBYCEyWNk5ROUEU1JzJDeMXR7Bzg3RbbmEWLaq3mdSQJOA94p4vL/RHN7SNHjfH2EeeciyZuvbbMrEHS1QTVUqnAvWa2UtItwCIzmwNcI+kcoAHYCVzavL6ksQRXNPNabPoRSQUEVWfLgKvidQwAS4vLSU9N4dARfiOic85FE7dAAmBmzwHPtUi7MeL5DQRtHtHWfZ8ojfNmdmrXlrJtSzdVMHlEDhlpqd25W+ec6zX8Nu021Dc2seKDCp/Iyjnn2uCBpA2rt1ZRU9/kAzU651wbPJC0YYmP+Oucc+3yQNKGpUUVDB2Ywcjc/okuinPO9VhxbWzv7SYOG8ABgzIJeho755yLxgNJG/7zlAMTXQTnnOvxvGrLOedcp3ggcc451ykeSJxzznWKBxLnnHOd4oHEOedcp3ggcc451ykeSJxzznWKBxLnnHOdIrOWs9/2PZJKgU0dWCUf2BGn4vR0yXzskNzHn8zHDsl9/K0d+xgza3eu8qQIJB0laZGZTUt0ORIhmY8dkvv4k/nYIbmPv7PH7lVbzjnnOsUDiXPOuU7xQBLd3YkuQAIl87FDch9/Mh87JPfxd+rYvY3EOedcp/gViXPOuU7xQOKcc65TPJBEkDRT0mpJ6yRdn+jyxJukUZJelvSupJWSvh2mD5b0oqS14d+8RJc1XiSlSloq6dlweZykN8Njf0JSeqLLGC+SciXNlvRe+B34RLJ89pK+G37n35H0mKTMvvzZS7pX0nZJ70SkRf2sFfhteB5cIemo9rbvgSQkKRW4EzgTmAzMkjQ5saWKuwbgv8zsEOA44JvhMV8P/MvMJgL/Cpf7qm8D70Ys/y9wW3js5cB/JKRU3eN24HkzOxiYQvA+9PnPXtJI4BpgmpkdBqQCF9K3P/v7gZkt0lr7rM8EJoaPK4A/tLdxDyT7TAfWmdkGM6sDHgfOTXCZ4srMtpjZkvB5FcGJZCTBcT8QZnsAOC8xJYwvSYXAZ4F7wmUBpwKzwyx9+dhzgJOAPwGYWZ2ZVZAknz3BNOP9JaUBWcAW+vBnb2bzgZ0tklv7rM8FHrTAAiBX0vC2tu+BZJ+RQHHEckmYlhQkjQWOBN4EhpnZFgiCDTA0cSWLq98A3weawuUhQIWZNYTLffk7MB4oBe4Lq/bukZRNEnz2ZvYB8CugiCCA7AIWkzyffbPWPusOnws9kOyjKGlJ0Tda0gDgL8B3zKwy0eXpDpLOArab2eLI5ChZ++p3IA04CviDmR0J7KEPVmNFE7YFnAuMA0YA2QTVOS311c++PR3+P/BAsk8JMCpiuRDYnKCydBtJ/QiCyCNm9tcweVvzpWz4d3uiyhdHJwDnSHqfoBrzVIIrlNywugP69negBCgxszfD5dkEgSUZPvtPAxvNrNTM6oG/AseTPJ99s9Y+6w6fCz2Q7LMQmBj23EgnaHybk+AyxVXYJvAn4F0z+7+Il+YAl4TPLwGe6e6yxZuZ3WBmhWY2luCzfsnMLgZeBs4Ps/XJYwcws61AsaSDwqRPAatIgs+eoErrOElZ4f9A87EnxWcfobXPeg7w1bD31nHAruYqsNb4ne0RJH2G4FdpKnCvmf0swUWKK0mfBF4F3mZfO8EPCdpJngRGE/zTfdHMWjbU9RmSTgG+Z2ZnSRpPcIUyGFgKfNnMahNZvniRNJWgo0E6sAH4GsGPyz7/2Uv6MfAlgp6LS4HLCdoB+uRnL+kx4BSC4eK3ATcBTxPlsw6D6+8IenlVA18zs0Vtbt8DiXPOuc7wqi3nnHOd4oHEOedcp3ggcc451ykeSJxzznWKBxLnnHOd4oHE9VmSGiUtC0d5XS7pWkkp4WvTJP02fJ4h6Z9h3i9JOjFcZ5mk/l1QjvNaGwBU0s2Svhc+zwxHYb2pC/b5vqT8zm7HuViktZ/FuV5rr5lNBZA0FHgUGATcFPaLb+4bfyTQLyLvXcCvzOy+WHYS9ruXmTW1kuU84FmCm95a20Y6wQgDi83sx7Hs17mewq9IXFIws+0EQ2JfHd6xe4qkZ8MA8zAwNbwCuRK4ALhR0iMAkq6TtDCcm+HHYdpYBXN4/B5YAoyStFvSz8KrnwWShkk6HjgH+GW4/QlRipdGcCPcWjP72HhXkr4h6RcRy5dKuiN8/rSkxeEV1BVR1h3bYg6K70m6OXw+QdLz4fqvSjo4TP+ignk6lkua3/F32yUbDyQuaZjZBoLv/NCItO0EdzW/amZTzeyPBENEXGdmF0s6nWBehunAVOBoSSeFqx9EMNz2kWa2iWDwvwVmNgWYD3zdzF6P2N5UM1sfpWjfBxrM7DutFH028PmI5S8BT4TPLzOzo4FpwDWShnTgLbkb+Fa4/veA34fpNwJnhMdxTge255KUV225ZBNtZNO2nB4+lobLAwgCSxGwKZyvoVkdQRUWBMOSnxbjPv4NfELSJDNb0/JFMyuVtCEc92gtQQB7LXz5GkmfC5+PCstW1t4OwxGfjwf+HNTMAZAR/n0NuF/SkwQDGjrXJg8kLmmE42g1EoxyekisqwE/D69UIrc1lmDo9Uj1tm/MoUZi//+aTzCx0D8knWhm0UZafYKgyu094Ckzs3CMsE8DnzCzakmvAJkt1mvgozUPza+nEMy/MbXljszsKknHEkz6tUzSVDNrNzi55OVVWy4pSCoA7gJ+F3Gyj8Vc4LLwFzySRobtKh1RBQxsK4OZ/QX4JfC8pNwoWf5K0Gg/i33VWoOA8jCIHEwwXXJL24ChkoZIygDOCvdXCWyU9MXwuCRpSvh8gpm9aWY3Ajv46JDizn2MBxLXl/Vv7v4L/BN4AehQjygze4Ggt9cbkt4maK9oMyhE8ThwnYKZCKM1tjfv6y6CgDFHUmaL18oJen2NMbO3wuTngTRJK4CfAJHVbM3r1QO3EIzo/CzBFU2zi4H/kLQcWMm+qaV/KentsJF+PrC8g8frkoyP/uucc65T/IrEOedcp3ggcc451ykeSJxzznWKBxLnnHOd4oHEOedcp3ggcc451ykeSJxzznXK/w8FlB0wQ6jAqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "# test_results = []\n",
    "\n",
    "X=final_counts\n",
    "y=final['Score']\n",
    "\n",
    "#####  SPLIT  #######\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=0.25, random_state=4)\n",
    "# print(\"Train Data Set is 60%: \")\n",
    "# print(X_train.shape)\n",
    "# print(y_train.shape)\n",
    "# print(\"*\"*12)\n",
    "# print(\"Test Data Set is 20%: \")\n",
    "# print(X_test.shape)\n",
    "# print(y_test.shape)\n",
    "# print(\"*\"*12)\n",
    "# print(\"CV Data Set is 20%: \")\n",
    "# print(X_cv.shape)\n",
    "# print(y_cv.shape)\n",
    "    \n",
    "#####  SPLIT  #######\n",
    "auc=[]\n",
    "ks=[]\n",
    "for i in range(1,100,4):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i, algorithm='brute')\n",
    "    knn.fit(X_train, y_train)\n",
    "    # y_pred = knn.predict(X_test)\n",
    "    proba = knn.predict_proba(X_cv)[:,1]\n",
    "    auc.append(roc_auc_score(y_cv, proba))\n",
    "    ks.append(i)\n",
    "# print(pd.DataFrame(maxx)[0])\n",
    "# print(maxx)\n",
    "# maxK=0\n",
    "# for i in maxx:\n",
    "#     if maxK<maxx[i][0]:\n",
    "#         maxK=maxx[i][0]\n",
    "\n",
    "# print(max(maxx[]))\n",
    "# print(\"Pre-def lib accuracy: \", metrics.accuracy_score(y_test, y_pred))\n",
    "ax=sns.lineplot(ks,auc)\n",
    "ax.set(xlabel='Differnt K values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different K value Curve')\n",
    "plt.show()\n",
    "# ####### AUC ########\n",
    "\n",
    "# from sklearn.metrics import roc_curve, auc\n",
    "# false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "# roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "# test_results.append(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks[auc.index(max(auc))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1dhfJZ7ZCAaR"
   },
   "source": [
    "## [4.2] Bi-Grams and n-Grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u1Ma2LBfCAaR",
    "outputId": "a5208a4c-c0ad-4360-f021-b2e8715610a4"
   },
   "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  (4986, 3144)\n",
      "the number of unique words including both unigrams and bigrams  3144\n"
     ]
    }
   ],
   "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",
    "# 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": "F--Xk5fhCAaV"
   },
   "source": [
    "## [4.3] TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6heiZFZ-CAaW",
    "outputId": "08103e90-4bd8-410e-b3dc-84a02e01aa33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['ability', 'able', 'able find', 'able get', 'absolute', 'absolutely', 'absolutely delicious', 'absolutely love', 'absolutely no', 'according']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (4986, 3144)\n",
      "the number of unique words including both unigrams and bigrams  3144\n",
      "[[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": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), 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])\n",
    "temp=final_tf_idf.toarray()\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XnzP-eZdCAaa"
   },
   "source": [
    "## [4.4] Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e-IuUZsTCAaa"
   },
   "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": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GIbKBSkRCAac",
    "outputId": "d72c6206-2c3f-4143-8c21-3f5b674310df",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('alternative', 0.9917705059051514), ('regular', 0.9917610883712769), ('snack', 0.9912378191947937), ('calorie', 0.9903585314750671), ('excellent', 0.989759087562561), ('tasty', 0.9893745183944702), ('ahoy', 0.9892535209655762), ('satisfying', 0.9890836477279663), ('looking', 0.9888262748718262), ('especially', 0.9888089299201965)]\n",
      "==================================================\n",
      "[('gourmet', 0.999315619468689), ('varieties', 0.9993099570274353), ('pure', 0.9993010759353638), ('starbucks', 0.9992761611938477), ('somewhat', 0.9992583990097046), ('enjoyed', 0.9992219805717468), ('fine', 0.9992106556892395), ('prefer', 0.9992098808288574), ('beef', 0.9992060661315918), ('remember', 0.9991855025291443)]\n"
     ]
    }
   ],
   "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": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JEJGArtUCAae",
    "outputId": "943e0fc6-83f8-455b-ba53-8dd05428fc92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  3817\n",
      "sample words  ['product', 'available', 'course', 'total', 'pretty', 'stinky', 'right', 'nearby', 'used', 'ca', 'not', 'beat', 'great', 'received', 'shipment', 'could', 'hardly', 'wait', 'try', 'love', 'call', 'instead', 'removed', 'easily', 'daughter', 'designed', 'printed', 'use', 'car', 'windows', 'beautifully', 'shop', 'program', 'going', 'lot', 'fun', 'everywhere', 'like', 'tv', 'computer', 'really', 'good', 'idea', 'final', 'outstanding', 'window', 'everybody', 'asks', 'bought', 'made']\n"
     ]
    }
   ],
   "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": "EPjGCg7UCAag"
   },
   "source": [
    "## [4.4.1] Converting text into vectors using wAvg W2V, TFIDF-W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oPxfYXhMCAag"
   },
   "source": [
    "#### [4.4.1.1] Avg W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sB4Y18rQCAag",
    "outputId": "c9f64dac-cc89-43e3-9820-fbc18c39a69e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 4986/4986 [00:04<00:00, 1181.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.09677556 -0.14455026  0.13659341  0.32349352  0.42822329  0.01876282\n",
      " -0.54895416 -0.12276022 -0.57074452  0.48999241  0.47242404 -0.11748011\n",
      " -0.21095067  0.22573409 -0.12275989 -0.04062062 -0.23705798 -0.38158838\n",
      " -0.0561033   0.31106871  0.41148341  0.27989355  0.08650331  0.1132743\n",
      "  0.06410525 -0.23231831 -0.12149962  0.22999239 -0.14595662  0.0667508\n",
      "  0.44429035 -0.05354458 -0.31396972 -0.53488503 -0.08642642 -0.27490074\n",
      "  0.24487231 -0.373858    0.59423537  0.45996311  0.31028521  0.19625957\n",
      "  0.71053487  0.35620382 -0.77343348  0.23572513  0.4082553   0.08480921\n",
      "  0.50801837 -0.35491438]\n",
      "50\n"
     ]
    }
   ],
   "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(sent_vectors[0])\n",
    "print(len(sent_vectors[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sKAEsZZLCAam"
   },
   "source": [
    "#### [4.4.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pAVTG3brCAao"
   },
   "outputs": [],
   "source": [
    "# S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "model = TfidfVectorizer()\n",
    "model.fit(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": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tyxhz3XQCAap",
    "outputId": "e72f3ca0-7d29-4657-a107-c5d678514cf3",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 4986/4986 [00:23<00:00, 212.45it/s]\n"
     ]
    }
   ],
   "source": [
    "# TF-IDF weighted Word2Vec\n",
    "tfidf_feat = model.get_feature_names() # tfidf words/col-names\n",
    "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
    "\n",
    "tfidf_sent_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\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\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",
    "        if word in w2v_words and word in tfidf_feat:\n",
    "            vec = w2v_model.wv[word]\n",
    "#             tf_idf = tf_idf_matrix[row, tfidf_feat.index(word)]\n",
    "            # to reduce the computation we are \n",
    "            # dictionary[word] = idf value of word in whole courpus\n",
    "            # sent.count(word) = tf valeus of word in this review\n",
    "            tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n",
    "            sent_vec += (vec * tf_idf)\n",
    "            weight_sum += tf_idf\n",
    "    if weight_sum != 0:\n",
    "        sent_vec /= weight_sum\n",
    "    tfidf_sent_vectors.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.08628934 -0.12353521  0.11386965  0.29242703  0.38744809  0.01387128\n",
      " -0.47811334 -0.10900359 -0.50312016  0.44104457  0.40877713 -0.09426763\n",
      " -0.18393843  0.19843384 -0.09986879 -0.03709504 -0.20524835 -0.33946113\n",
      " -0.0486698   0.26311891  0.36022074  0.25178525  0.0764532   0.11701793\n",
      "  0.05197116 -0.21097848 -0.10450247  0.19493878 -0.12452618  0.05029162\n",
      "  0.3839128  -0.04993013 -0.26466741 -0.47722935 -0.0730545  -0.24625002\n",
      "  0.21040747 -0.32048562  0.51694382  0.41018146  0.26867051  0.17092989\n",
      "  0.62504991  0.30848498 -0.67671882  0.20551604  0.35591394  0.07653864\n",
      "  0.44735701 -0.31314572]\n"
     ]
    }
   ],
   "source": [
    "print(tfidf_sent_vectors[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IjMcxjHfCAas"
   },
   "source": [
    "# [5] Applying TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SPCsfz2fCAat"
   },
   "source": [
    "<ol> \n",
    "    <li> you need to plot 4 tsne plots with each of these feature set\n",
    "        <ol>\n",
    "            <li>Review text, preprocessed one converted into vectors using (BOW)</li>\n",
    "            <li>Review text, preprocessed one converted into vectors using (TFIDF)</li>\n",
    "            <li>Review text, preprocessed one converted into vectors using (AVG W2v)</li>\n",
    "            <li>Review text, preprocessed one converted into vectors using (TFIDF W2v)</li>\n",
    "        </ol>\n",
    "    </li>\n",
    "    <li> <font color='blue'>Note 1: The TSNE accepts only dense matrices</font></li>\n",
    "    <li> <font color='blue'>Note 2: Consider only 5k to 6k data points </font></li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2114,
     "status": "ok",
     "timestamp": 1547365772849,
     "user": {
      "displayName": "Applied AI Course",
      "photoUrl": "https://lh3.googleusercontent.com/-EsJzSyawCkQ/AAAAAAAAAAI/AAAAAAAADag/xYU9KO6AZf4/s64/photo.jpg",
      "userId": "06629147635963609455"
     },
     "user_tz": -330
    },
    "id": "Z379u6ElCAat",
    "outputId": "47a20627-9d10-45c8-fe40-ecd0726e33c5"
   },
   "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": [
    "# https://github.com/pavlin-policar/fastTSNE you can try this also, this version is little faster than sklearn \n",
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris['data']\n",
    "y = iris['target']\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)\n",
    "\n",
    "X_embedding = tsne.fit_transform(x)\n",
    "# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix\n",
    "\n",
    "for_tsne = np.hstack((X_embedding, y.reshape(-1,1)))\n",
    "for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])\n",
    "colors = {0:'red', 1:'blue', 2:'green'}\n",
    "plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ejF91OEjCAaw"
   },
   "source": [
    "## [5.1] Applying TNSE on Text BOW vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rnetKp45CAaw"
   },
   "outputs": [],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label\n",
    "    \n",
    "##############################################################################################\n",
    "\n",
    "bow_reviews_5000 = final_counts.toarray()\n",
    "scores_5000 = final['Score']\n",
    "\n",
    "bow_tsne = TSNE(n_components=2, perplexity=80, learning_rate=200)\n",
    "\n",
    "bow_embedding = bow_tsne.fit_transform(bow_reviews_5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.875x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for_tsne = np.hstack((bow_embedding, np.array(scores_5000).reshape(-1,1)))\n",
    "# ######################\n",
    "\n",
    "for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])\n",
    "sns.FacetGrid(for_tsne_df, hue='Score', size=6).map(plt.scatter, 'Dimension_x','Dimension_y').add_legend()\n",
    "plt.title('TSNE on Bag of Words of Amazon Reviews')\n",
    "plt.show()\n",
    "\n",
    "# ######################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b_-boR2SCAay"
   },
   "source": [
    "## [5.1] Applying TNSE on Text TFIDF vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6eBTrer9CAay"
   },
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.875x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label\n",
    "    \n",
    "    #########################################################################################\n",
    "\n",
    "tfidf_5000 = final_tf_idf.toarray()\n",
    "print(tfidf_5000)\n",
    "scores_5000 = final['Score']\n",
    "tfidf_tsne = TSNE(n_components=2, perplexity=40, learning_rate=200)\n",
    "\n",
    "tfidf_embedding = tfidf_tsne.fit_transform(tfidf_5000)\n",
    "\n",
    "for_tsne = np.hstack((tfidf_embedding, np.array(scores_5000).reshape(-1,1)))\n",
    "# ######################\n",
    "\n",
    "for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])\n",
    "sns.FacetGrid(for_tsne_df, hue='Score', size=6).map(plt.scatter, 'Dimension_x','Dimension_y').add_legend()\n",
    "plt.title('TSNE on TF-IDF of Amazon Reviews')\n",
    "plt.show()\n",
    "\n",
    "# ######################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zln2L0SUCAa0"
   },
   "source": [
    "## [5.3] Applying TNSE on Text Avg W2V vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-YnpPa54CAa0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.875x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "w2v_5000 = sent_vectors\n",
    "# print(w2v_5000)\n",
    "score_5000 = final['Score']\n",
    "w2v_tsne = TSNE(n_components=2, perplexity=40, learning_rate=200)\n",
    "\n",
    "w2v_embedding = w2v_tsne.fit_transform(w2v_5000)\n",
    "\n",
    "for_tsne = np.hstack((w2v_embedding, np.array(scores_5000).reshape(-1,1)))\n",
    "# #########################################################################\n",
    "\n",
    "for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])\n",
    "sns.FacetGrid(for_tsne_df, hue='Score', size=6).map(plt.scatter, 'Dimension_x','Dimension_y').add_legend()\n",
    "plt.title('TSNE on avg W2V of Amazon Reviews')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RBaVF4aHCAa4"
   },
   "source": [
    "## [5.4] Applying TNSE on Text TFIDF weighted W2V vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xYysToufCAa4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.875x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label\n",
    "\n",
    "    \n",
    "tfidf_w2v_5000 = tfidf_sent_vectors\n",
    "# print(w2v_5000)\n",
    "score_5000 = final['Score']\n",
    "tfidf_w2v_tsne = TSNE(n_components=2, perplexity=40, learning_rate=200)\n",
    "\n",
    "tfidf_w2v_embedding = tfidf_w2v_tsne.fit_transform(tfidf_w2v_5000)\n",
    "\n",
    "for_tsne = np.hstack((tfidf_w2v_embedding, np.array(score_5000).reshape(-1,1)))\n",
    "# #########################################################################\n",
    "\n",
    "for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])\n",
    "sns.FacetGrid(for_tsne_df, hue='Score', size=6).map(plt.scatter, 'Dimension_x','Dimension_y').add_legend()\n",
    "plt.title('TSNE on Text TFIDF weighted W2V vectors of Amazon Reviews')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jilexiiyCAa6"
   },
   "source": [
    "# [6] Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TroM2UQUCAa7"
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   "source": [
    "* BoW TSNE:-\n",
    "    Both classes are concentrated under x<=10 but for x>10 we get some positive feedbacks.\n",
    "\n",
    "* tf-idf TSNE-\n",
    "    After trying different perpexlities we are unable to get a differentiation between two different classes.\n",
    "    \n",
    "* TNSE on Text TFIDF weighted W2V vectors\n",
    "    After trying different perpexlities we are unable to get a differentiation between two different classes.\n",
    "    \n",
    "* TNSE on Text Avg W2V vectors\n",
    "    After trying different perpexlities we are unable to get a differentiation between two different classes.\n",
    "    "
   ]
  }
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