4. Amazon Fine Food Reviews Analysis_NB.ipynb

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    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews\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",
    "<b>Objective:-</b>\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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    "## [1.1] Loading the data\n",
    "\n",
    "The dataset is available in two forms\n",
    "1. .csv file\n",
    "2. SQLite Database\n",
    "\n",
    "In order to load the data, We have used the SQLITE dataset as it 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",
    "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 matplotlib.pyplot as pyplt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\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 sklearn.naive_bayes import  MultinomialNB\n",
    "from nltk.stem.porter import PorterStemmer\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",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:00.476983Z",
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    "id": "-lrJx9N9MQvT"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (525814, 10)\n"
     ]
    },
    {
     "data": {
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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...  "
      ]
     },
     "execution_count": 2,
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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",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 \"\"\", con) \n",
    "\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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   "outputs": [],
   "source": [
    "sample_data = filtered_data.head(500000) #Sampled amazon fine foood reviews filtered data to 50k datapoints for time effiecieny"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "## 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:"
   ]
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   "outputs": [
    {
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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>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": 4,
     "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": "95Aa3bAYMQvb"
   },
   "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": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:05.260333Z",
     "start_time": "2019-01-26T23:43:04.253787Z"
    },
    "colab": {},
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    "id": "0wUj2BDzMQvc"
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   "outputs": [],
   "source": [
    "#Sorting data according to ProductId in ascending order\n",
    "sorted_data=sample_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:07.196203Z",
     "start_time": "2019-01-26T23:43:05.454700Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "hYd69x4KMQve"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348262, 10)"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:07.539006Z",
     "start_time": "2019-01-26T23:43:07.532011Z"
    },
    "colab": {},
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    "id": "_UVH2UNwMQvi"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.6524"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking to see how much % of data still remains\n",
    "(final['Id'].size*1.0)/(sample_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "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"
   ]
  },
  {
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    {
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       "  <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",
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       "    <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": 8,
     "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": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:08.239642Z",
     "start_time": "2019-01-26T23:43:08.064706Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "kThrWN9FMQvq"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:08.488466Z",
     "start_time": "2019-01-26T23:43:08.459482Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "lvw544BEMQvs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(348260, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    293516\n",
       "0     54744\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 10,
     "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": "3-ZDuet8MQvw"
   },
   "source": [
    "## Text Preprocessing: Stemming, stop-word removal and Lemmatization.\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": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:08.786296Z",
     "start_time": "2019-01-26T23:43:08.767305Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "I2NRwIj6MQvz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "TITLE: Chicken Soup with Rice<br />AUTHOR: Maurice Sendak<br />REVIEWER: Josh Grossman, Colonel {r}, U.S.A.R., M.C., M.D., FACP<br />Rural physician/author/educator-Mentor U.S.M.L.E. Step 3...<br />>Who would believe that I have memorized this book?<br />>Who would believe that I can recite any month in any order?<br />>Who would believe that I read my copy to tatters while reading to my children now professional adults?<br />>Who would believe that my grandchildren read their copy to tatters?<br />I can be confident that generations of children will enjoy \"Chicken Soup with Rice\"<br />Bravo!<br />Thank you!<br />Respectfully submitted...\n"
     ]
    }
   ],
   "source": [
    "# find sentences containing HTML tags\n",
    "import re\n",
    "i=0;\n",
    "for sent in final['Text'].values:\n",
    "    if (len(re.findall('<.*?>', sent))):\n",
    "        print(i)\n",
    "        print(sent)\n",
    "        break;\n",
    "    i += 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:09.097118Z",
     "start_time": "2019-01-26T23:43:09.072131Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "hSYsO2neMQv2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"haven't\", 'myself', 'shan', \"wasn't\", 'won', 'ours', 'ain', 'such', 'because', 'no', 'hasn', 'only', 'here', 'more', 'shouldn', 'having', 'over', 'herself', 'being', 'to', 'her', 'by', 'she', 'its', 'each', 'on', 'just', 'who', 'than', 'about', \"that'll\", \"you've\", 'if', 'that', 'had', 'be', 'most', 'themselves', 'during', 'few', 'should', 'yours', \"needn't\", 'are', 'y', 'below', \"mightn't\", 'do', 'not', \"mustn't\", 'weren', 'from', 'did', 'the', \"doesn't\", 'where', 'needn', 'those', 'you', 'didn', 'can', 'mightn', 'were', \"you'd\", \"couldn't\", 'but', 'again', 'it', 'me', 've', 'doesn', \"isn't\", 'yourself', \"shan't\", 'been', \"weren't\", 'i', 'was', 'with', 'hadn', 'both', 'and', 'until', \"should've\", 'd', 'further', \"shouldn't\", 'into', 'hers', 'don', \"you're\", 'while', 'above', 't', 'has', 'how', 'own', 'what', 'off', 'whom', 'when', 'nor', 'any', 'my', 'down', 'some', 'haven', 'this', 'before', 'o', 'through', 'ourselves', 'their', 's', 'll', \"hasn't\", 'in', \"you'll\", \"aren't\", 'why', 're', 'his', 'between', 'an', 'out', 'once', 'is', 'under', 'these', 'so', 'or', 'your', 'aren', 'couldn', 'm', 'after', 'wasn', 'same', \"wouldn't\", 'for', 'we', 'other', 'him', \"hadn't\", \"she's\", 'at', 'very', 'wouldn', 'isn', 'which', 'yourselves', \"didn't\", 'up', 'himself', 'have', 'does', 'now', 'he', 'itself', 'doing', 'there', 'our', 'then', 'will', 'of', 'mustn', 'all', 'them', 'against', 'too', 'they', 'am', \"don't\", 'ma', 'as', 'a', \"won't\", \"it's\", 'theirs'}\n",
      "************************************\n",
      "tasti\n"
     ]
    }
   ],
   "source": [
    "\n",
    "stop = set(stopwords.words('english')) #set of stopwords\n",
    "sno = nltk.stem.SnowballStemmer('english') #initialising the snowball stemmer\n",
    "\n",
    "def cleanhtml(sentence): #function to clean the word of any html-tags\n",
    "    cleanr = re.compile('<.*?>')\n",
    "    cleantext = re.sub(cleanr, ' ', sentence)\n",
    "    return cleantext\n",
    "def cleanpunc(sentence): #function to clean the word of any punctuation or special characters\n",
    "    cleaned = re.sub(r'[?|!|\\'|\"|#]',r'',sentence)\n",
    "    cleaned = re.sub(r'[.|,|)|(|\\|/]',r' ',cleaned)\n",
    "    return  cleaned\n",
    "print(stop)\n",
    "print('************************************')\n",
    "print(sno.stem('tasty'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:18.752544Z",
     "start_time": "2019-01-26T23:43:18.736554Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "BUGzVaKbMQv5"
   },
   "outputs": [],
   "source": [
    "#Code for implementing step-by-step the checks mentioned in the pre-processing phase\n",
    "# this code takes a while to run as it needs to run on 500k sentences.\n",
    "if not os.path.isfile('final.sqlite'):\n",
    "    final_string=[]\n",
    "    all_positive_words=[] # store words from +ve reviews here\n",
    "    all_negative_words=[] # store words from -ve reviews here.\n",
    "    for i, sent in enumerate(tqdm(final['Text'].values)):\n",
    "        filtered_sentence=[]\n",
    "        #print(sent);\n",
    "        sent=cleanhtml(sent) # remove HTMl tags\n",
    "        for w in sent.split():\n",
    "            # we have used cleanpunc(w).split(), one more split function here because consider w=\"abc.def\", cleanpunc(w) will return \"abc def\"\n",
    "            # if we dont use .split() function then we will be considring \"abc def\" as a single word, but if you use .split() function we will get \"abc\", \"def\"\n",
    "            for cleaned_words in cleanpunc(w).split():\n",
    "                if((cleaned_words.isalpha()) & (len(cleaned_words)>2)):    \n",
    "                    if(cleaned_words.lower() not in stop):\n",
    "                        s=(sno.stem(cleaned_words.lower())).encode('utf8') #snoball stemmer\n",
    "                        filtered_sentence.append(s)\n",
    "                        if (final['Score'].values)[i] == 1: \n",
    "                            all_positive_words.append(s) #list of all words used to describe positive reviews\n",
    "                        if(final['Score'].values)[i] == 0:\n",
    "                            all_negative_words.append(s) #list of all words used to describe negative reviews reviews\n",
    "        str1 = b\" \".join(filtered_sentence) #final string of cleaned words\n",
    "        #print(\"***********************************************************************\")\n",
    "        final_string.append(str1)\n",
    "\n",
    "    #############---- storing the data into .sqlite file ------########################\n",
    "    final['CleanedText']=final_string #adding a column of CleanedText which displays the data after pre-processing of the review \n",
    "    final['CleanedText']=final['CleanedText'].str.decode(\"utf-8\")\n",
    "        # store final table into an SQlLite table for future.\n",
    "    conn = sqlite3.connect('final.sqlite')\n",
    "    c=conn.cursor()\n",
    "    conn.text_factory = str\n",
    "    final.to_sql('Reviews', conn,  schema=None, if_exists='replace', \\\n",
    "                 index=True, index_label=None, chunksize=None, dtype=None)\n",
    "    conn.close()\n",
    "    \n",
    "    \n",
    "    with open('positive_words.pkl', 'wb') as f: # 'wb' instead 'w' for binary file\n",
    "        pickle.dump(all_positive_words, f)      # dump data to f\n",
    "    with open('negitive_words.pkl', 'wb') as f: # 'wb' instead 'w' for binary file\n",
    "        pickle.dump(all_negative_words, f)      # dump data to f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:23.805975Z",
     "start_time": "2019-01-26T23:43:19.686342Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "bU88yTG9MQv-",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "if os.path.isfile('final.sqlite'):\n",
    "    conn = sqlite3.connect('final.sqlite')\n",
    "    final = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 \"\"\", conn)\n",
    "    conn.close()\n",
    "else:\n",
    "    print(\"Please the above cell\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting dataset based on 'Time' feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:24.401822Z",
     "start_time": "2019-01-26T23:43:24.032045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348260, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_reviews = final.sort_values('Time', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "final_reviews.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:24.941545Z",
     "start_time": "2019-01-26T23:43:24.724639Z"
    }
   },
   "outputs": [],
   "source": [
    "#split data into train, cross validate and test \n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = final_reviews['CleanedText']\n",
    "Y = final_reviews['Score']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33, random_state=0)\n",
    "X_tr, X_cv, Y_tr, Y_cv = train_test_split(X_train, Y_train, test_size=.33, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:29.788826Z",
     "start_time": "2019-01-26T23:43:29.757843Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train, Y_train (233334,) (233334,)\n",
      "X_test, Y_test (114926,) (114926,)\n",
      "X_tr, Y_tr (156333,) (156333,)\n",
      "X_cv, Y_cv (77001,) (77001,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train, Y_train', X_train.shape, Y_train.shape)\n",
    "print('X_test, Y_test', X_test.shape, Y_test.shape)\n",
    "print('X_tr, Y_tr', X_tr.shape, Y_tr.shape)\n",
    "print('X_cv, Y_cv', X_cv.shape, Y_cv.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multinomial Naive Bayes with simple CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:32.205950Z",
     "start_time": "2019-01-26T23:43:32.193955Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from collections import Counter\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:34.429832Z",
     "start_time": "2019-01-26T23:43:34.412845Z"
    }
   },
   "outputs": [],
   "source": [
    "def mulnb(X_train,X_cv,Y_train,Y_cv):\n",
    "    best_alpha=0\n",
    "    max_roc_auc=-1\n",
    "    pred_cv = []\n",
    "    pred_train = []\n",
    "    alpha=[10000,5000,1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001,0.00005,0.00001] \n",
    "    for i in alpha[-19:]:\n",
    "        mulbnb = MultinomialNB(alpha=i)\n",
    "        mulbnb.fit(X_train,Y_train)\n",
    "        probs = mulbnb.predict_proba(X_cv)\n",
    "        prob = mulbnb.predict_proba(X_train)\n",
    "        probs = probs[:,1]\n",
    "        prob = prob[:,1]\n",
    "        auc_score_cv = roc_auc_score(Y_cv,probs)\n",
    "        auc_score_train = roc_auc_score(Y_train,prob)\n",
    "        print(i,\" ------> \",auc_score_cv)\n",
    "        pred_cv.append(auc_score_cv)\n",
    "        pred_train.append(auc_score_train)\n",
    "        if(max_roc_auc<auc_score_cv):\n",
    "            max_roc_auc=auc_score_cv\n",
    "            best_alpha=i\n",
    "    print(f\"\\n Best alpha Value {best_alpha} with highest roc_auc Score is {max_roc_auc}\")\n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.xscale('log')\n",
    "    plt.plot(alpha, pred_cv,'r-', label = 'CV Data')\n",
    "    plt.plot(alpha,pred_train,'g-', label ='Train Data')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(r'Auc Score v/s $\\alpha$')\n",
    "    plt.xlabel(r\"alpha values\",fontsize=12)\n",
    "    plt.ylabel(\"roc_auc\",fontsize=12)\n",
    "    plt.show()\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_cv,probs)\n",
    "    # plot no skill\n",
    "    pyplt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    pyplt.plot(fpr, tpr, marker='.')\n",
    "    #plt.plot(k,pred_cv)\n",
    "    pyplt.title(\"Line Plot of ROC Curve on Train Data\")\n",
    "    pyplt.ylabel('True Positive Rate')\n",
    "    pyplt.xlabel('False Positive Rate')\n",
    "    pyplt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:37.522847Z",
     "start_time": "2019-01-26T23:43:37.495844Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikitplot.metrics as skplt\n",
    "def testing(X_train,Y_train,X_test,Y_test,optimal_alpha):\n",
    "    bnb = MultinomialNB(optimal_alpha,fit_prior=True, class_prior=None)\n",
    "    bnb.fit(X_train,Y_train)\n",
    "    probs = bnb.predict_proba(X_test) \n",
    "    # keep probabilities for the positive outcome only\n",
    "    probs = probs[:, 1]\n",
    "    print(\"AUC Score\",roc_auc_score(Y_test,probs))\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_test,probs)\n",
    "    # plot no skill\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    plt.plot(fpr, tpr, marker='.')\n",
    "    plt.title(\"Line Plot of ROC Curve on Test Data\")\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')    \n",
    "    plt.show\n",
    "    prediction=bnb.predict(X_test)\n",
    "    skplt.plot_confusion_matrix(Y_test,prediction)\n",
    "    print(\"macro f1 score for data :\",metrics.f1_score(Y_test, prediction, average = 'macro'))\n",
    "    print(\"micro f1 scoore for data:\",metrics.f1_score(Y_test, prediction, average = 'micro'))\n",
    "    print(\"hamming loss for data:\",metrics.hamming_loss(Y_test,prediction))\n",
    "    print(\"Precision recall report for data:\\n\",metrics.classification_report(Y_test, prediction))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:43:39.053850Z",
     "start_time": "2019-01-26T23:43:39.047854Z"
    }
   },
   "outputs": [],
   "source": [
    "def imp_feature(model,classifier):\n",
    "    neg = classifier.feature_log_prob_[0,:].argsort()\n",
    "    pos = classifier.feature_log_prob_[1,:].argsort()\n",
    "    top_pos_words = np.take(model.get_feature_names(),pos)\n",
    "    top_neg_words = np.take(model.get_feature_names(),neg)\n",
    "    imp_df = pd.DataFrame(columns = ['Pos_Words','Pos_Importance','Neg_Words','Neg_Importance'])\n",
    "    imp_df['Pos_Words'] = top_pos_words[::-1]\n",
    "    imp_df['Pos_Importance'] = np.take(classifier.feature_log_prob_[1,:],pos)[::-1]\n",
    "    imp_df['Neg_Words'] = top_neg_words[::-1]\n",
    "    imp_df['Neg_Importance'] = np.take(classifier.feature_log_prob_[0,:],neg)[::-1]\n",
    "    return imp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Techniques for vectorization :--"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hXA02Hw0MQv_"
   },
   "source": [
    "##  Bag of Words (BoW) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:44:01.344261Z",
     "start_time": "2019-01-26T23:43:41.329924Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "giCwNM_rMQwA"
   },
   "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  (156333, 47172)\n",
      "CV Data Size:  (77001, 47172)\n",
      "Test Data Size:  (114926, 47172)\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "bow_train = count_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(bow_train))\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "#print(\"the number of unique words \", final_counts.get_shape()[1])\n",
    "bow_cv = count_vect.transform(X_cv)\n",
    "bow_test = count_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-26T23:44:02.837408Z",
     "start_time": "2019-01-26T23:44:02.326700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (156333, 47172)\n",
      "CV Data Size:  (77001, 47172)\n",
      "Test Data Size:  (114926, 47172)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "bow_train=preprocessing.normalize(bow_train)\n",
    "bow_cv=preprocessing.normalize(bow_cv)\n",
    "bow_test=preprocessing.normalize(bow_test)\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:06:09.200602Z",
     "start_time": "2019-01-27T00:06:00.742486Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  ------>  0.636370646160004\n",
      "5000  ------>  0.642259388004025\n",
      "1000  ------>  0.6612953583871227\n",
      "500  ------>  0.6696437273597264\n",
      "100  ------>  0.6916900843222149\n",
      "50  ------>  0.7073860347835936\n",
      "10  ------>  0.7850972417666611\n",
      "5  ------>  0.8389043183678297\n",
      "1  ------>  0.9227426881602911\n",
      "0.5  ------>  0.9311163824200236\n",
      "0.1  ------>  0.9348375799288724\n",
      "0.05  ------>  0.9345252754760249\n",
      "0.01  ------>  0.9312802455484984\n",
      "0.005  ------>  0.9289873138159687\n",
      "0.001  ------>  0.9221275730861038\n",
      "0.0005  ------>  0.9186788921536588\n",
      "0.0001  ------>  0.9100850120932407\n",
      "5e-05  ------>  0.9062960834324971\n",
      "1e-05  ------>  0.8976023668853019\n",
      "\n",
      " Best alpha Value 0.1 with highest roc_auc Score is 0.9348375799288724\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal alpha \n",
    "mulnb(bow_train,bow_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NB simple CV Over the unseen or Test data in BoW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:06:17.592224Z",
     "start_time": "2019-01-27T00:06:16.465797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9356708485671656\n",
      "macro f1 score for data : 0.6150328604181582\n",
      "micro f1 scoore for data: 0.8686633137845223\n",
      "hamming loss for data: 0.13133668621547778\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.18      0.30     18099\n",
      "           1       0.87      1.00      0.93     96827\n",
      "\n",
      "   micro avg       0.87      0.87      0.87    114926\n",
      "   macro avg       0.90      0.59      0.62    114926\n",
      "weighted avg       0.88      0.87      0.83    114926\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6dWvGjBmDyWSicePGTJgwAbPZzPz589myZQuenp4kJCQQFhZGenq6021LrL/M1YuI4ZlMzr3+KC0tjZ07d7Jy5UqWL1/OyZMnSUpKIj4+nuTkZGw2G5s2bWLfvn3s2LGDlJQUZs2axXPPPQdwRW1Loh6ciDhU1lPUbdu20aRJE5588kksFgujRo1i9erVtG7dGoDIyEi2b99OSEgIERERmEwmgoODKS4uJjMzk3379jndNigoyGEdCjgRcaisl+CysrI4fvw4ixYt4ujRowwaNAibzWYPTF9fX3JycrBYLAQEBNg/d2n5lbRVwIlImZjLmHABAQGEhobi5eVFaGgoVatW5eTJk/b1ubm5VK9eHT8/P3Jzcy9b7u/vj9lsdrptifWXqXoR+Uswm01Ovf7o9ttvZ+vWrdhsNjIyMjh//jzt2rUjLS0NgNTUVMLDw2nZsiXbtm3DarVy/PhxrFYrQUFBNGvWzOm2JVEPTkQcKusgaqdOnfj666954IEHsNlsJCYmcsMNNzB+/HhmzZpFaGgoUVFReHh4EB4eTs+ePbFarSQmJgIwevRop9uWxGSz2WxlO4SyO5dvrehdylU6eTbf1SXIFWpS1+eqt9F10Q6n2n04sPVV76s8qAcnIg65+X2+jgOuZ8+e/zNEfGlkY9WqVeVemIi4ngn3TjiHATdr1qyKrENEKiE3fxyc44C7/vrrAcjIyGDGjBlkZWURFRXFjTfeaF8nIsbm7g+8LPU2kfHjx/PPf/6TgoICwsPDeeGFFyqiLhGpBMwmk1OvyqrUgLtw4QLt2rXDZDLZb9gTkb+Gss5FrSxKHUX18vJi69atWK1Wdu3ahZeXV0XUJSKVgLs/LqnUHtzzzz/P2rVrycrKYunSpUycOLECyhKRysDwPbi6desyYMAAfv31Vxo3bky9evUqoi4RqQQ8KnN6OaHUgFuwYAFbt26lefPmLFu2jC5dutC3b98KKE1EXM3dT1FLDbjU1FSSk5Mxm80UFRXxyCOPKOBE/iLc/C6R0q/BBQUFcf78eQAKCwtLnb0vIsZhMpmcelVWpU7VOnPmjP0G34MHD172wDkRMbZKnF1O0VQtEXGoMvfOnFHqVK309HQ+/vhjCgsLATh16hSTJk2qmOpExKU83PwiXKnX4EaPHg3Ad999x9GjRzl79my5FyUilYPJyVdlVWrAeXt7M2DAAOrUqcPUqVP57bffKqIuEakE3H0uaqm3idhsNk6fPk1eXh55eXlkZ2dXRF0iUglU4uxySqk9uCFDhrBhwwbuv/9+OnfuTGRkZEXUJSKVgGFvE7mkVatWtGrVCoDOnTuXe0EiUnlU4uxyisOAi4iIcPihbdu2lUsxIlK5uPsoqsOAK88Q8/LU17G6m1vvHeXqEuQKnd85/6q3UZlPP52hb9USEYfcvSuigBMRh9y9B+dUQFssFg4cOEBeXl551yMilYjZ5Nyrsiq1B/fxxx+zaNEiiouL6dKlCyaTicGDB1dEbSLiYu4+yFBqD27ZsmWsXr2agIAABg8ezMaNGyuiLhGpBAzfgzObzXh5edlv6KtWrVpF1CUilYCbX4IrPeDCw8MZPnw4GRkZJCYm0rx584qoS0Qqgco8z9QZpQbc8OHDSU1NpVmzZjRs2JBOnTpVRF0iUgm4+20ipda/bt06MjMzqVWrFtnZ2axbt64i6hKRSsDwXxt48OBB4OJTRfbv309AQADdu3cv98JExPXcfRS11IAbMWKE/e82m40BAwaUa0EiUnm4eb6VHnAFBQX2v58+fZqjR4+Wa0EiUnkYfpDh0s29NpsNb29v+vXrVxF1iUgl4Ob5VnrAPfXUU8TExFRELSJSybj7KWqpo6gpKSkVUYeIVEImJ/9UVk5dg+vevTshISGYzRfz8MUXXyz3wkTE9a720Y1nzpwhNjaWpUuX4unpyZgxYzCZTDRu3JgJEyZgNpuZP38+W7ZswdPTk4SEBMLCwkhPT3e6bYn1l1bgM888c3VHKCJu62oel1RYWEhiYiLe3t4AJCUlER8fT5s2bUhMTGTTpk0EBwezY8cOUlJSOHHiBEOHDmXNmjVX1LYkDgMuPj6eOXPm0Lp16zIfoIi4t6u5Bjdt2jR69erF4sWLAdi3b589TyIjI9m+fTshISFERERgMpkIDg6muLiYzMzMK2obFBTkuH5HKzIzM8t+ZCJiCGWdybB27VqCgoLo0KGDfZnNZrP3CH19fcnJycFiseDn52dvc2n5lbQticMe3JEjR5g1a9afrhs+fHiJGxURYyjrfXBr1qzBZDLx5Zdfsn//fkaPHn1Zpyk3N5fq1avj5+dHbm7uZcv9/f3t1/udaVti/Y5WeHt7ExIS8qcvEflr8DA79/qjN998kxUrVrB8+XKaNm3KtGnTiIyMJC0tDYDU1FTCw8Np2bIl27Ztw2q1cvz4caxWK0FBQTRr1szptiVx2IOrVasWPXr0uIofjYi4O/M1vAVk9OjRjB8/nlmzZhEaGkpUVBQeHh6Eh4fTs2dPrFYriYmJV9y2JCabzWb7sxXTpk1j9OjR1+zgfi+/qFw2K+UosNUQV5cgV+hafG3ggi9+dard4Dv+ftX7Kg8Oe3DlFW4i4j7cfSaDvjZQRBwy/GR7EfnrcvN8U8CJiGOGf+CliPx1uft3MijgRMShq5mLWhko4ETEIfeONwWciJRAo6giYljuHW8KOBEpgVmjqCJiVBpFFRHD0iiqiBiWe8ebAk5ESqAenIgYlocCTkSMyr3jTQEnIiVw8w6cAk5EHLuWjyx3BQWciDikHpyIGJZJPTgRMSqNooqIYbl5vingRMQxBZyIGJauwYmIYbn505IUcCLimJ7oKyKGpVPUv5jCwkIG/Ptx0tN/5cKFC4xJGEfrNm15cuATZGVlUVxczKuvvUFow4bMnDGNlFUr8a9eneHPjKLrfd1cXb6heVXxZPFzvQm5vibncvOJn7oam83GvLG98KriyYWCIvqMeY3M7FxS5gygZg0fCousnL9QQPchC2lx0w2smTuQnw+fBmBJylbe/vQ7po2I5Y4WoVitNsbMeocvdx9y8ZFWHJ2iOrB7925mzpzJ8uXLy2sXLrHyzRUE1azJ0teXc+bMGdq2uo2OHe+i58P/4oEHH+LzLZ9x4MAP5OXlsXplMqlfpAHQqcMddOx0Fz4+Pi4+AuN6PPYOLHkXuPPRF2nc4G/MHv0QVTzNTJj/Hjv2/Er3zi1o3OBvpH3/Cw3r1aLlP1+47PMtmtbjpRWbmbt8s31Z8ybX0zYshA5xM2lYvzZvJD1G+39Nr+hDcxl378GVyxOJlyxZwrhx47hw4UJ5bN6lYh94kAnPPW9/7+npyZdfbufY0aN0jbqbVclvEnlnR374YT8d7uyIt7c33t7eNGzcmD3ff+/Cyo3vptC6fLp9HwA/pZ+iRdMbqB3kT9fI5nyy5ClaN/87X+/9lb8F+RPg78OauQPZtPRp7u1wCwC3Na1Pl4ib2fBqPAsnPIKfT1WOnzpLXn4BVb08qe7rTVFRsSsPscKZTM69KqtyCbj69eszb9688ti0y/n5+eHv709OTg6P9HyACc9NJv3XXwkMDOTDTzZSr359XpwxjVtuac72rank5ORw5swZvvryC/Lycl1dvqF9f+AY90ZeDKvWzf9O7UB/bm4UzOa0H4h6Yi6BNXzoHd0GryoezF2+iYeGL6bXiCVMfyaW2oF+fLM3nYQ567in3xx+OXqGsQO6UlRkxWqzsfud8XywaChzlm9y8VFWLJOTr8qqXAIuKioKT0/jXt47cuQIXe7uxCP/iqPXw49Qs2ZN7ou+H4Cu90Xz3bffcFPTpgwcPISYbvcyZuQIWrVuQ82atVxcubG9/u6X5Fjy+WTJU3SNbM63+9I5ZzlP6jc/AfBR6l5aNqvPyTPnWJKyjeJiK6ezLOz+4ShN/l6H9Zt3s3P/EQDWf7abW2+8gX9Ftybjt3M0i55I0/smMHZAV4Jr13DlYVYoD5PJqVdl5e5fmlPhMjIyiO76DyYnTePRxx4HoF37CD756EMAtm1NpWmzmzl9+jS/nfmNzZ9vY+bsuRw9coSbb7nFlaUbXvjNDfhi1yGinpjL+s92c+job/x0+DTtb2sIQETLRuw/eJK72tzEiukXf3e+1bxo1ug6fvjlJO8teJLwmxsA0Kn1jezcf5isc3lYzhdgtdrIycvnQkERvj5VXXaMFc7Nu3DG7WaVk+lTp3A2K4ukF54n6YWL1+KWLH2dwQP+zeKXF1KjRg2WLU8mICCAXw8don3bVnh5eZE0bQYeHh4urt7Yfj58isTB9xHfpzNnc/IY9FwytQL9mDPmITw9zPx6/Axj575LYVEx97Rryuevj8BqszFh3nucOZvLsCmrmD3mIQoKi8k4c44nn19J7vkLtGvRkM+WDcfDbOatj77hp/RTrj7UCuPugwwmm81mK48NHz16lOHDh7N69er/WZdfVB57lPIU2GqIq0uQK3R+5/yr3saOQ9lOtWsdWjlP28utB3fDDTf8abiJiPtw7/6bTlFFpCRunnAKOBFxSHNRRcSwyhpvhYWFJCQkcOzYMQoKChg0aBCNGjVizJgxmEwmGjduzIQJEzCbzcyfP58tW7bg6elJQkICYWFhpKenO922JAo4EXGsjAm3fv16AgICmDFjBllZWfTo0YObbrqJ+Ph42rRpQ2JiIps2bSI4OJgdO3aQkpLCiRMnGDp0KGvWrCEpKcnptiVRwImIQ2W9TaRLly5ERUXZ33t4eLBv3z5at24NQGRkJNu3byckJISIiAhMJhPBwcEUFxeTmZl5RW2DgoIc1qEbfUXEobLORfX19cXPzw+LxcKwYcOIj4/HZrNh+n+NfX19ycnJwWKx4Ofnd9nncnJyrqhtSRRwIuLQ1Uy2P3HiBH369CEmJobo6GjM5v8fN7m5uVSvXh0/Pz9yc3MvW+7v739FbUuigBMRh0xO/vmj3377jccff5yRI0fywAMPANCsWTPS0i4+Piw1NZXw8HBatmzJtm3bsFqtHD9+HKvVSlBQ0BW1LYmuwYmIQ2W9S2TRokWcO3eOBQsWsGDBAgDGjh3L5MmTmTVrFqGhoURFReHh4UF4eDg9e/bEarWSmJgIwOjRoxk/frxTbUusv7ymapVEU7Xcj6ZquZ9rMVVr71GLU+1uucGv9EYuoB6ciDjm3vf5KuBExDF3f5qIAk5EHNKXzoiIcSngRMSodIoqIobl5g8TUcCJiGNunm8KOBEpgZsnnAJORBzSAy9FxLDcO94UcCJSEjdPOAWciDik20RExLDc/BKcAk5EHFPAiYhh6RRVRAxLPTgRMSw3zzcFnIg4ph6ciBiYeyecAk5EHNIDL0XEsHSKKiKGpdtERMS43DvfFHAi4pib55sCTkQc0zU4ETEsk5snnAJORBxy73hTwIlICdy8A6eAExHHdJuIiBiWenAiYlgKOBExLJ2iiohhqQcnIobl5vmmgBORErh5wingRMQhXYMTEcPSAy9FxLgUcCJiVO5+imqy2Ww2VxchIlIezK4uQESkvCjgRMSwFHAiYlgKuGvAarWSmJhIz549iYuLIz093dUliZN2795NXFycq8uQcqJR1Gtg48aNFBQU8NZbb7Fr1y6mTp3KwoULXV2WlGLJkiWsX7+eatWquboUKSfqwV0D3377LR06dACgRYsW7N2718UViTPq16/PvHnzXF2GlCMF3DVgsVjw8/Ozv/fw8KCoqMiFFYkzoqKi8PTUSYyRKeCuAT8/P3Jzc+3vrVar/uGIVAIKuGugZcuWpKamArBr1y6aNGni4opEBDTIcE3cc889bN++nV69emGz2ZgyZYqrSxIRNFVLRAxMp6giYlgKOBExLAWciBiWAk5EDEsBJyKGpYBzA2lpabRr1464uDji4uJ46KGHWL58eZm2NXPmTNauXcv+/fuZP3++w3YbNmwgIyPDqW2mpqYyZsyY/6n56aefdviZtWvXMnPmTKe2fyVtRX5P98G5ibZt2zJ79mwACgoK6NKlCzExMVSvXr1M22vatClNmzZ1uP6NN95g4sSJ1KlTp0zbF6kMFHBuyGKxYDab8fDwIC4ujsDAQM6dO8fixYuZOHEi6enpWK1W4uPjadNGvtdkAAADgUlEQVSmDZ988gkLFy4kKCiIwsJCQkNDSUtLY9WqVcyePZuUlBRWrlyJ1Wqlc+fONG/enP379zN69GiSk5N56623eP/99zGZTHTt2pU+ffpw8OBBEhISqFatGtWqVaNGjRoO612xYgWffvopRUVF+Pv72ye479q1i0cffRSLxcLQoUPp2LEjO3bsYPbs2Xh4eFCvXj0mTZpUUT9WMSAFnJv46quviIuLw2QyUaVKFcaPH4+vry8A0dHR3HPPPSQnJxMYGMiUKVPIysqid+/efPDBB8yYMYOUlBQCAgLo37//Zds9c+aM/bFBXl5eTJ06lVatWtG0aVMmTpzI4cOH+fDDD0lOTsZkMtG3b18iIiKYO3cuw4YNo3379ixevJhDhw79ad1Wq5WzZ8+ybNkyzGYz/fr1Y8+ePQBUq1aNxYsXk5mZyYMPPkiHDh0YP348ycnJ1KxZkzlz5vDOO+9oXq+Umf7LcRO/P0X9o5CQEAB+/PFHvv32W77//nsAioqK+O233/Dz8yMwMBCA22677bLPHjlyhMaNG+Pt7Q1AQkLCZet//PFHjh8/Tt++fQHIzs7m8OHD/PTTT4SFhQEX5+I6Cjiz2UyVKlUYPnw4Pj4+nDx50v6kldtvvx2TyUTNmjXx9/cnKyuLU6dOER8fD0B+fj7t27enfv36V/SzErlEAWcAJtPFr3YLDQ2lbt26DBw4kPz8fBYuXEj16tXJyckhMzOToKAg9uzZQ926de2frV+/PocOHaKgoAAvLy+GDRvG2LFjMZlM2Gw2QkNDadSoEa+88gomk4lly5bRpEkTQkND2blzJ5GRkSU+/+6HH35g48aNpKSkcP78eWJjY7k0O/BST+706dPk5eURGBhI3bp1WbBgAf7+/mzatAkfHx9OnDhRjj89MTIFnIH06tWLcePG0bt3bywWC4888gheXl4kJSXRr18/atSo8T+ne0FBQTzxxBP07t0bk8lEp06dqFOnDrfddhujRo1i6dKltGvXjocffpiCggLCwsKoU6cOEyZM4Omnn+bVV18lKCiIqlWr/mlNDRo0oFq1asTGxuLl5UXt2rU5deoUcLGH1qdPH/Ly8pg0aRIeHh6MHTuW/v37Y7PZ8PX1Zfr06Qo4KTNNthcRw9J9cCJiWAo4ETEsBZyIGJYCTkQMSwEnIoalgBMRw1LAiYhhKeBExLD+DxMWk+OlMZzSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = brute force, optimal_k=11\n",
    "testing(bow_train,Y_tr,bow_test,Y_test,optimal_alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:06:23.415007Z",
     "start_time": "2019-01-27T00:06:22.889157Z"
    }
   },
   "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>Pos_Words</th>\n",
       "      <th>Pos_Importance</th>\n",
       "      <th>Neg_Words</th>\n",
       "      <th>Neg_Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>like</td>\n",
       "      <td>-4.408478</td>\n",
       "      <td>tast</td>\n",
       "      <td>-4.126758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tast</td>\n",
       "      <td>-4.412099</td>\n",
       "      <td>like</td>\n",
       "      <td>-4.246824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>love</td>\n",
       "      <td>-4.433405</td>\n",
       "      <td>product</td>\n",
       "      <td>-4.390704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>great</td>\n",
       "      <td>-4.458662</td>\n",
       "      <td>one</td>\n",
       "      <td>-4.752713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>good</td>\n",
       "      <td>-4.476377</td>\n",
       "      <td>flavor</td>\n",
       "      <td>-4.764328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>flavor</td>\n",
       "      <td>-4.598010</td>\n",
       "      <td>would</td>\n",
       "      <td>-4.856262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>use</td>\n",
       "      <td>-4.708053</td>\n",
       "      <td>tri</td>\n",
       "      <td>-4.901650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product</td>\n",
       "      <td>-4.714912</td>\n",
       "      <td>buy</td>\n",
       "      <td>-5.005655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>-4.797766</td>\n",
       "      <td>good</td>\n",
       "      <td>-5.008839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tri</td>\n",
       "      <td>-4.882972</td>\n",
       "      <td>coffe</td>\n",
       "      <td>-5.043479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>coffe</td>\n",
       "      <td>-4.922982</td>\n",
       "      <td>order</td>\n",
       "      <td>-5.072395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tea</td>\n",
       "      <td>-4.969096</td>\n",
       "      <td>use</td>\n",
       "      <td>-5.102608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>make</td>\n",
       "      <td>-5.025104</td>\n",
       "      <td>get</td>\n",
       "      <td>-5.134089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>get</td>\n",
       "      <td>-5.080651</td>\n",
       "      <td>dont</td>\n",
       "      <td>-5.235991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>buy</td>\n",
       "      <td>-5.213327</td>\n",
       "      <td>box</td>\n",
       "      <td>-5.302261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>price</td>\n",
       "      <td>-5.286251</td>\n",
       "      <td>tea</td>\n",
       "      <td>-5.373534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>best</td>\n",
       "      <td>-5.289779</td>\n",
       "      <td>food</td>\n",
       "      <td>-5.388235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>time</td>\n",
       "      <td>-5.296074</td>\n",
       "      <td>even</td>\n",
       "      <td>-5.390599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>food</td>\n",
       "      <td>-5.301066</td>\n",
       "      <td>amazon</td>\n",
       "      <td>-5.484577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>realli</td>\n",
       "      <td>-5.330851</td>\n",
       "      <td>eat</td>\n",
       "      <td>-5.505956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>order</td>\n",
       "      <td>-5.333103</td>\n",
       "      <td>much</td>\n",
       "      <td>-5.507798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>amazon</td>\n",
       "      <td>-5.339127</td>\n",
       "      <td>time</td>\n",
       "      <td>-5.533751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>would</td>\n",
       "      <td>-5.343920</td>\n",
       "      <td>make</td>\n",
       "      <td>-5.538938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>find</td>\n",
       "      <td>-5.360609</td>\n",
       "      <td>bag</td>\n",
       "      <td>-5.540281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>eat</td>\n",
       "      <td>-5.393413</td>\n",
       "      <td>realli</td>\n",
       "      <td>-5.541417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>much</td>\n",
       "      <td>-5.473519</td>\n",
       "      <td>love</td>\n",
       "      <td>-5.563907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>store</td>\n",
       "      <td>-5.497075</td>\n",
       "      <td>look</td>\n",
       "      <td>-5.574797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>littl</td>\n",
       "      <td>-5.510175</td>\n",
       "      <td>disappoint</td>\n",
       "      <td>-5.576006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>also</td>\n",
       "      <td>-5.551246</td>\n",
       "      <td>purchas</td>\n",
       "      <td>-5.587835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>dont</td>\n",
       "      <td>-5.563069</td>\n",
       "      <td>packag</td>\n",
       "      <td>-5.608922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47142</th>\n",
       "      <td>oonc</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mukka</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47143</th>\n",
       "      <td>emeraldforestsugar</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mul</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47144</th>\n",
       "      <td>ooncentr</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mulatilay</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47145</th>\n",
       "      <td>embrass</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mulder</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47146</th>\n",
       "      <td>emblazon</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mulit</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47147</th>\n",
       "      <td>embezzl</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mulitpl</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47148</th>\n",
       "      <td>raider</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mulk</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47149</th>\n",
       "      <td>ooooop</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mullah</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47150</th>\n",
       "      <td>embaress</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mullein</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47151</th>\n",
       "      <td>ooam</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>mulligan</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47152</th>\n",
       "      <td>empathet</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multiblend</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47153</th>\n",
       "      <td>scamthi</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multibran</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47154</th>\n",
       "      <td>enchalada</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multicarton</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47155</th>\n",
       "      <td>encyclopaed</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multicultur</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47156</th>\n",
       "      <td>bezzera</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multidimension</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47157</th>\n",
       "      <td>raisenett</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multidisciplinari</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47158</th>\n",
       "      <td>bfn</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multiflavor</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47159</th>\n",
       "      <td>bfs</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multifuncut</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47160</th>\n",
       "      <td>enchlada</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multigallon</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47161</th>\n",
       "      <td>trunatur</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multigrano</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47162</th>\n",
       "      <td>enbed</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multilay</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47163</th>\n",
       "      <td>emphaisi</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multilevel</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47164</th>\n",
       "      <td>onnale</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multimet</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47165</th>\n",
       "      <td>truss</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multipag</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47166</th>\n",
       "      <td>bhindi</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multipak</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167</th>\n",
       "      <td>scammer</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multiport</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47168</th>\n",
       "      <td>scammish</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multipurpos</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47169</th>\n",
       "      <td>truthinlabel</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multitid</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47170</th>\n",
       "      <td>sitll</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>multitut</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47171</th>\n",
       "      <td>tfg</td>\n",
       "      <td>-15.680902</td>\n",
       "      <td>aaa</td>\n",
       "      <td>-14.050289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47172 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pos_Words  Pos_Importance          Neg_Words  Neg_Importance\n",
       "0                    like       -4.408478               tast       -4.126758\n",
       "1                    tast       -4.412099               like       -4.246824\n",
       "2                    love       -4.433405            product       -4.390704\n",
       "3                   great       -4.458662                one       -4.752713\n",
       "4                    good       -4.476377             flavor       -4.764328\n",
       "5                  flavor       -4.598010              would       -4.856262\n",
       "6                     use       -4.708053                tri       -4.901650\n",
       "7                 product       -4.714912                buy       -5.005655\n",
       "8                     one       -4.797766               good       -5.008839\n",
       "9                     tri       -4.882972              coffe       -5.043479\n",
       "10                  coffe       -4.922982              order       -5.072395\n",
       "11                    tea       -4.969096                use       -5.102608\n",
       "12                   make       -5.025104                get       -5.134089\n",
       "13                    get       -5.080651               dont       -5.235991\n",
       "14                    buy       -5.213327                box       -5.302261\n",
       "15                  price       -5.286251                tea       -5.373534\n",
       "16                   best       -5.289779               food       -5.388235\n",
       "17                   time       -5.296074               even       -5.390599\n",
       "18                   food       -5.301066             amazon       -5.484577\n",
       "19                 realli       -5.330851                eat       -5.505956\n",
       "20                  order       -5.333103               much       -5.507798\n",
       "21                 amazon       -5.339127               time       -5.533751\n",
       "22                  would       -5.343920               make       -5.538938\n",
       "23                   find       -5.360609                bag       -5.540281\n",
       "24                    eat       -5.393413             realli       -5.541417\n",
       "25                   much       -5.473519               love       -5.563907\n",
       "26                  store       -5.497075               look       -5.574797\n",
       "27                  littl       -5.510175         disappoint       -5.576006\n",
       "28                   also       -5.551246            purchas       -5.587835\n",
       "29                   dont       -5.563069             packag       -5.608922\n",
       "...                   ...             ...                ...             ...\n",
       "47142                oonc      -15.680902              mukka      -14.050289\n",
       "47143  emeraldforestsugar      -15.680902                mul      -14.050289\n",
       "47144            ooncentr      -15.680902          mulatilay      -14.050289\n",
       "47145             embrass      -15.680902             mulder      -14.050289\n",
       "47146            emblazon      -15.680902              mulit      -14.050289\n",
       "47147             embezzl      -15.680902            mulitpl      -14.050289\n",
       "47148              raider      -15.680902               mulk      -14.050289\n",
       "47149              ooooop      -15.680902             mullah      -14.050289\n",
       "47150            embaress      -15.680902            mullein      -14.050289\n",
       "47151                ooam      -15.680902           mulligan      -14.050289\n",
       "47152            empathet      -15.680902         multiblend      -14.050289\n",
       "47153             scamthi      -15.680902          multibran      -14.050289\n",
       "47154           enchalada      -15.680902        multicarton      -14.050289\n",
       "47155         encyclopaed      -15.680902        multicultur      -14.050289\n",
       "47156             bezzera      -15.680902     multidimension      -14.050289\n",
       "47157           raisenett      -15.680902  multidisciplinari      -14.050289\n",
       "47158                 bfn      -15.680902        multiflavor      -14.050289\n",
       "47159                 bfs      -15.680902        multifuncut      -14.050289\n",
       "47160            enchlada      -15.680902        multigallon      -14.050289\n",
       "47161            trunatur      -15.680902         multigrano      -14.050289\n",
       "47162               enbed      -15.680902           multilay      -14.050289\n",
       "47163            emphaisi      -15.680902         multilevel      -14.050289\n",
       "47164              onnale      -15.680902           multimet      -14.050289\n",
       "47165               truss      -15.680902           multipag      -14.050289\n",
       "47166              bhindi      -15.680902           multipak      -14.050289\n",
       "47167             scammer      -15.680902          multiport      -14.050289\n",
       "47168            scammish      -15.680902        multipurpos      -14.050289\n",
       "47169        truthinlabel      -15.680902           multitid      -14.050289\n",
       "47170               sitll      -15.680902           multitut      -14.050289\n",
       "47171                 tfg      -15.680902                aaa      -14.050289\n",
       "\n",
       "[47172 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnb = MultinomialNB(alpha = 0.1)\n",
    "mnb.fit(bow_train,Y_tr)\n",
    "imp_feature(count_vect,mnb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:07:30.239278Z",
     "start_time": "2019-01-27T00:06:24.804607Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "The shape of out text TFIDF vectorizer  (156333, 1700503)\n",
      "CV Data Size:  (77001, 1700503)\n",
      "Test Data Size:  (114926, 1700503)\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2))\n",
    "tfidf_train = tf_idf_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(tfidf_train))\n",
    "print(\"The shape of out text TFIDF vectorizer \",tfidf_train.get_shape())\n",
    "tfidf_cv = tf_idf_vect.transform(X_cv)\n",
    "tfidf_test = tf_idf_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:07:33.427407Z",
     "start_time": "2019-01-27T00:07:32.879730Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (156333, 1700503)\n",
      "CV Data Size:  (77001, 1700503)\n",
      "Test Data Size:  (114926, 1700503)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "tfidf_train=preprocessing.normalize(tfidf_train)\n",
    "tfidf_cv=preprocessing.normalize(tfidf_cv)\n",
    "tfidf_test=preprocessing.normalize(tfidf_test)\n",
    "print(\"The shape of out text BOW vectorizer \",tfidf_train.get_shape())\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:08:00.885487Z",
     "start_time": "2019-01-27T00:07:35.934364Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  ------>  0.6461512740658859\n",
      "5000  ------>  0.6473422255661663\n",
      "1000  ------>  0.6541082355075842\n",
      "500  ------>  0.6594912039398987\n",
      "100  ------>  0.679685448236734\n",
      "50  ------>  0.6923646644108793\n",
      "10  ------>  0.7316951113639796\n",
      "5  ------>  0.7538849522372294\n",
      "1  ------>  0.8287088319571705\n",
      "0.5  ------>  0.8713004566704378\n",
      "0.1  ------>  0.9375367876517429\n",
      "0.05  ------>  0.9430939915889895\n",
      "0.01  ------>  0.9339782961633863\n",
      "0.005  ------>  0.9249419180950971\n",
      "0.001  ------>  0.8995744381203381\n",
      "0.0005  ------>  0.8885836403858436\n",
      "0.0001  ------>  0.8654511661583819\n",
      "5e-05  ------>  0.8567242961986493\n",
      "1e-05  ------>  0.8391959259654784\n",
      "\n",
      " Best alpha Value 0.05 with highest roc_auc Score is 0.9430939915889895\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_alpha\n",
    "mulnb(tfidf_train,tfidf_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:08:33.254339Z",
     "start_time": "2019-01-27T00:08:30.941204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9443887916254162\n",
      "macro f1 score for data : 0.7134150287115463\n",
      "micro f1 scoore for data: 0.8906078694116215\n",
      "hamming loss for data: 0.10939213058837861\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.33      0.49     18099\n",
      "           1       0.89      1.00      0.94     96827\n",
      "\n",
      "   micro avg       0.89      0.89      0.89    114926\n",
      "   macro avg       0.91      0.66      0.71    114926\n",
      "weighted avg       0.89      0.89      0.87    114926\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8PN8UcCLinDo4ETEtBZyImNaFnEV9+eWX+eSTTygqKuLuu++mQ4cOJCQkYBgGYWFhTJ48GYvFwrx589i4cSPe3t4kJibSunVrsrKyXB5bZv0Vrl5ETM8wXHv9UWZmJt988w1vvvkmS5Ys4ZdffiE5OZn4+HhSU1NxOBysX7+eXbt2sWXLFtLS0pg1axZPPvkkwHmNLYs6OBFxqqK7qJs2baJp06Y8/PDD2Gw2Hn/8cZYvX06HDh0AiIyMJCMjg5CQECIiIjAMg+DgYEpKSsjOzmbXrl0uj7VarU7rUMCJiFMVPQSXk5PDoUOHWLBgAQcOHGD48OE4HI7SwPTz8yM3NxebzUZgYGDp536bfj5jFXAiUiGWCiZcYGAgoaGh+Pj4EBoaSs2aNfnll19K5+fl5VGnTh38/f3Jy8s7Z3pAQAAWi8XlsWXWX6HqReSSYLEYLr3+qF27dnz22Wc4HA6OHDnC6dOn6dSpE5mZmQCkp6cTHh5O27Zt2bRpE3a7nUOHDmG327FarbRo0cLlsWVRByciTlX0JGrXrl358ssvufPOO3E4HCQlJXHFFVcwadIkZs2aRWhoKFFRUXh5eREeHs6AAQOw2+0kJSUBMH78eJfHlsVwOByOim1CxZ2wFVf1KuUCnSrQ78zThNT3veBl9FqwxaVxHwzrcMHrqgzq4ETEKQ+/ztd5wA0YMOBPp4h/O7OxbNmySi9MRNzPwLMTzmnAzZo1qyrrEJFqyMMfB+c84C6//HIAjhw5wowZM8jJySEqKopmzZqVzhMRc/P0B16We5nIpEmT+Mc//kFhYSHh4eH861//qoq6RKQasBiGS6/qqtyAO3PmDJ06dcIwjNIL9kTk0lDRe1Gri3LPovr4+PDZZ59ht9vZtm0bPj4+VVGXiFQDnv64pHI7uKeeeoqVK1eSk5PDwoULmTJlShWUJSLVgek7uEaNGjF06FB++uknwsLCuPLKK6uiLhGpBryqc3q5oNyAmz9/Pp999hmtWrVi8eLF9OjRg3vvvbcKShMRd/P0XdRyAy49PZ3U1FQsFgvFxcXcc889CjiRS4SHXyVS/jE4q9XK6dOnASgqKir37n0RMQ/DMFx6VVfl3qp14sSJ0gt89+7de84D50TE3KpxdrlEt2qJiFPVuTtzRbm3amVlZfHRRx9RVFQEwNGjR5k6dWrVVCcibuXl4Qfhyj0GN378eAC+/vprDhw4wK+//lrpRYlI9WC4+Kquyg04X19fhg4dSsOGDZk+fTrHjx+virpEpBrw9HtRy71MxOFwcOzYMfLz88nPz+fkyZNVUZeIVAPVOLtcUm4HN3LkSNauXcsdd9xBt27diIyMrIq6RKQaMO1lIr9p37497du3B6Bbt26VXpCIVB/VOLtc4jTgIiIinH5o06ZNlVKMiFQvnn4W1WnAVWaI+fnqu248zRVd4t1dgpyn09/Mu+BlVOfdT1coaUTEKU//ZngFnIg45ekdnEsBbbPZ+O6778jPz6/sekSkGrEYrr2qq3I7uI8++ogFCxZQUlJCjx49MAyDESNGVEVtIuJmnn6SodwObvHixSxfvpzAwEBGjBjBunXrqqIuEakGTN/BWSwWfHx8Si/oq1WrVlXUJSLVgIcfgis/4MLDwxkzZgxHjhwhKSmJVq1aVUVdIlINVOf7TF1RbsCNGTOG9PR0WrRowTXXXEPXrl2roi4RqQY8/TKRcutftWoV2dnZ1K9fn5MnT7Jq1aqqqEtEqgHTf23g3r17gbNPFdm9ezeBgYH06dOn0gsTEffz9LOo5Qbc2LFjS//scDgYOnRopRYkItWHh+db+QFXWFhY+udjx45x4MCBSi1IRKoP059k+O3iXofDga+vLw888EBV1CUi1YCH51v5AffII48QExNTFbWISDXj6buo5Z5FTUtLq4o6RKQaMlz8r7py6Rhcnz59CAkJwWI5m4fPPfdcpRcmIu7nfYEXwp04cYJ+/fqxcOFCvL29SUhIwDAMwsLCmDx5MhaLhXnz5rFx40a8vb1JTEykdevWZGVluTy2zPrLK/Cxxx67sC0UEY91IY9LKioqIikpCV9fXwCSk5OJj4+nY8eOJCUlsX79eoKDg9myZQtpaWkcPnyYUaNGsWLFivMaWxanARcfH8+cOXPo0KFDhTdQRDzbhRyDe+aZZ4iNjSUlJQWAXbt2leZJZGQkGRkZhISEEBERgWEYBAcHU1JSQnZ29nmNtVqtzut3NiM7O7viWyYiplDROxlWrlyJ1WqlS5cupdMcDkdpR+jn50dubi42mw1/f//SMb9NP5+xZXHawf3888/MmjXrL+eNGTOmzIWKiDlU9Dq4FStWYBgGX3zxBbt372b8+PHnNE15eXnUqVMHf39/8vLyzpkeEBBQerzflbFl1u9shq+vLyEhIX/5EpFLg5fFtdcfvfHGGyxdupQlS5bQvHlznnnmGSIjI8nMzAQgPT2d8PBw2rZty6ZNm7Db7Rw6dAi73Y7VaqVFixYujy2L0w6ufv369O3b9wJ+NCLi6SwX8RKQ8ePHM2nSJGbNmkVoaChRUVF4eXkRHh7OgAEDsNvtJCUlnffYshgOh8PxVzOeeeYZxo8ff9E27vcKiitlsVKJgtqPdHcJcp4uxtcGzv/8J5fGjbjx6gteV2Vw2sFVVriJiOfw9DsZ9LWBIuKU6W+2F5FLl4fnmwJORJwz/QMvReTS5enfyaCAExGnLuRe1OpAASciTnl2vCngRKQMOosqIqbl2fGmgBORMlh0FlVEzEpnUUXEtHQWVURMy7PjTQEnImVQBycipuWlgBMRs/LseFPAiUgZPLyBU8CJiHMX85Hl7qCAExGn1MGJiGkZ6uBExKx0FlVETMvD800BJyLOKeBExLR0DE5ETMvDn5akgBMR5/REXxExLe2iXoKOHj3KjR3b8f6HawmyWnl42EPk5ORQUlLCq4teJ/Saa1jz0Yf866knAWjTpi1z5r7o8U9mqO58aniT8uRAQi6vx6m8AuKnL8fhcDB3Qiw+Nbw5U1jMoIRFZJ/MY8rD0dzSsRkOh4Oxz77NV7uyqO3rwwsTBnB1cD18angz5pk0vtqVBUAt3xq8/9JIhj2Zyp6fjrh5S6uOdlGd2L59OzNnzmTJkiWVtQq3KCoqYuSIodSqVQuACQmPM+Duf3Jn/7v4dOMGvvvuWxpcdhmJCeNYs24j9evX57mZz3L8+HEaNGjg5urN7f5+N2LLP8NNg58j7KrLmD3+Lmp4W5g871227PiJPt2uJ+yqyyg4U0SHVlcTOWgmjf9mJW3OEDoOmM6jg2/lfz8c5sFJS2gZFkyrppfz1a4s2rZozAsTYrn8skB3b2KV8/QOrlKeSPzKK68wceJEzpw5UxmLd6uExx/joSHD+NvfggH44osMDh44QK+oW1mW+gaRN93M5i8+59qWrUgYN5ZuN3eh4WUNFW5V4O+hjfg4YxcA32cd5frmV9DAGkCvyFaseeUROrS6mi93/sT27w4Q/fCLADQOtnL0RC4A3W9sTmFRMatffJgnHurBus93A1CzhjexY1Iuqc7tN4bh2qu6qpSAa9y4MXPnzq2MRbvVktcW06BBA7rfFlU6LeunnwgKCuKDNeu4snFjnpvxDMePHyd94wamJT/DO+99yLwX5vD9nj1urPzS8N/vDtIzsiUAHVpdTYOgAK5tEswnmd8S9dDzBNWtzcDojgCUlNiZ8nA0K58fxlsffgVAvUA/AuvU5o6HX+T99J0kj+kLwBfb93HgyK/u2Sg3M1x8VVeVEnBRUVF4e5vv8N5rixeyft1abut2M//dvo0H7huEl5cXt0ffAUCv26P5eutX1KtXj3bh7WnUqBH+/v507hLJ9u3b3Fy9+b32zhfk2gpY88oj9IpsxdZdWZyynSb9q+8B+DB9J21bNC4dP+XFdwm9bQKPDr6VkCvqk/1rHu9/ugOADz7dcc7YS5WXYbj0qq48/UtzqtS6Dems/eRTPl6/kdbXXc+ri16n5+29WfPhBwBs+iyd5i2upU3bduzatZPjx49TXFzMlszNNG/ews3Vm1/4tVfx+bZ9RD30PKs3bGffgeN8v/8YndtcA0BE2ybs3vsLN7VvyuyEuwAoKCyiqLgEu91x9rMR154d264Ju/cedtu2VBse3sKZr82qYtOffY4RQx8k5eWXqFu3LouXpBIUFMTUacnc0evsrmy//ndxbcuWbq7U/H7Yf5SkEbcTP6gbv+bmM/zJVOoH+TMn4S68vSz8dOgEE55/hxK7nX7d2/DJokfxslh4+a10sg6d4NlX1/BS0j1sfG0sRcUlPDDxdXdvktt5+kkGw+FwOCpjwQcOHGDMmDEsX778T/MKiitjjVKZgtqPdHcJcp5OfzPvgpexZd9Jl8Z1CK17weuqDJXWwV1xxRV/GW4i4jk8u3/TLqqIlMXDE04BJyJO6V5UETGtisZbUVERiYmJHDx4kMLCQoYPH06TJk1ISEjAMAzCwsKYPHkyFouFefPmsXHjRry9vUlMTKR169ZkZWW5PLYsCjgRca6CCbd69WoCAwOZMWMGOTk59O3bl7///e/Ex8fTsWNHkpKSWL9+PcHBwWzZsoW0tDQOHz7MqFGjWLFiBcnJyS6PLYsCTkScquhlIj169CAq6v/u+PHy8mLXrl106NABgMjISDIyMggJCSEiIgLDMAgODqakpITs7OzzGmu1Wp3WoQt9RcSpit6L6ufnh7+/PzabjdGjRxMfH4/D4Sh9oo6fnx+5ubnYbDb8/f3P+Vxubu55jS2LAk5EnLqQm+0PHz7MoEGDiImJITo6Govl/+ImLy+POnXq4O/vT15e3jnTAwICzmtsWRRwIuKU4eJ/f3T8+HHuv/9+xo0bx5133glAixYtyMzMBCA9PZ3w8HDatm3Lpk2bsNvtHDp0CLvdjtVqPa+xZdExOBFxqqJXiSxYsIBTp04xf/585s+fD8CECROYNm0as2bNIjQ0lKioKLy8vAgPD2fAgAHY7XaSkpIAGD9+PJMmTXJpbJn1V9atWmXRrVqeR7dqeZ6LcavWzgM2l8a1vMK//EFuoA5ORJzz7Ot8FXAi4pynP01EASciTulLZ0TEvBRwImJW2kUVEdPy8IeJKOBExDkPzzcFnIiUwcMTTgEnIk7pgZciYlqeHW8KOBEpi4cnnAJORJzSZSIiYloefghOAScizingRMS0tIsqIqalDk5ETMvD800BJyLOqYMTERPz7IRTwImIU3rgpYiYlnZRRcS0dJmIiJiXZ+ebAk5EnPPwfFPAiYhzOgYnIqZleHjCKeBExCnPjjcFnIiUwcMbOAWciDiny0RExLTUwYmIaSngRMS0tIsqIqalDk5ETMvD800BJyJl8PCEU8CJiFM6BicipqUHXoqIeSngRMSsPH0X1XA4HA53FyEiUhks7i5ARKSyKOBExLQUcCJiWgq4i8But5OUlMSAAQOIi4sjKyvL3SWJi7Zv305cXJy7y5BKorOoF8G6desoLCzkrbfeYtu2bUyfPp2XXnrJ3WVJOV555RVWr15NrVq13F2KVBJ1cBfB1q1b6dKlCwDXX389O3fudHNF4orGjRszd+5cd5chlUgBdxHYbDb8/f1L33t5eVFcXOzGisQVUVFReHtrJ8bMFHAXgb+/P3l5eaXv7Xa7/scRqQYUcBdB27ZtSU9PB2Dbtm00bdrUzRWJCOgkw0XRvXt3MjIyiI2NxeFw8PTTT7u7JBFBt2qJiIlpF1VETEsBJyKmpYATEdNSwImIaSngRMS0FHAeIDMzk06dOhEXF0dcXBx33XUXS5YsqdCyZs6cycqVK9m9ezfz5s1zOm7t2rUcOXLEpWWmp6eTkJDwp5offfRRp59ZuXIlM2fOdGn55zNW5Pd0HZyHuOGGG5g9ezYAhYWF9OjRg5iYGOrUqVOh5TVv3pzmzZs7nf/6668zZcoUGjZsWKHli1QHCjgPZLPZsFgseHl5ERcXR1BQEKdOnSIlJYUpU6aQlZWF3W4nPj6ejh07smbNGl566SWsVitFRUWEhoaSmZnJsmXLmD17Nmlpabz55pvY7Xa6detGq1at2L17N+PHjyc1NZW33nqL9957D8Mw6NWrF4MGDWLv3r0kJiZSq1YtatWqRd0QS23jAAADKElEQVS6dZ3Wu3TpUj7++GOKi4sJCAgovcF927ZtDB48GJvNxqhRo7j55pvZsmULs2fPxsvLiyuvvJKpU6dW1Y9VTEgB5yE2b95MXFwchmFQo0YNJk2ahJ+fHwDR0dF0796d1NRUgoKCePrpp8nJyWHgwIG8//77zJgxg7S0NAIDAxkyZMg5yz1x4kTpY4N8fHyYPn067du3p3nz5kyZMoX9+/fzwQcfkJqaimEY3HvvvURERPD8888zevRoOnfuTEpKCvv27fvLuu12O7/++iuLFy/GYrHwwAMPsGPHDgBq1apFSkoK2dnZ9O/fny5dujBp0iRSU1OpV68ec+bM4T//+Y/u65UK098cD/H7XdQ/CgkJAWDPnj1s3bqV//73vwAUFxdz/Phx/P39CQoKAqBNmzbnfPbnn38mLCwMX19fABITE8+Zv2fPHg4dOsS9994LwMmTJ9m/fz/ff/89rVu3Bs7ei+ss4CwWCzVq1GDMmDHUrl2bX375pfRJK+3atcMwDOrVq0dAQAA5OTkcPXqU+Ph4AAoKCujcuTONGzc+r5+VyG8UcCZgGGe/2i00NJRGjRoxbNgwCgoKeOmll6hTpw65ublkZ2djtVrZsWMHjRo1Kv1s48aN2bdvH4WFhfj4+DB69GgmTJiAYRg4HA5CQ0Np0qQJ//73vzEMg8WLF9O0aVNCQ0P55ptviIyMLPP5d99++y3r1q0jLS2N06dP069fP367O/C3Tu7YsWPk5+cTFBREo0aNmD9/PgEBAaxfv57atWtz+PDhSvzpiZkp4EwkNjaWiRMnMnDgQGw2G/fccw8+Pj4kJyfzwAMPULdu3T/t7lmtVh566CEGDhyIYRh07dqVhg0b0qZNGx5//HEWLlxIp06duPvuuyksLKR169Y0bNiQyZMn8+ijj/Lqq69itVqpWbPmX9Z01VVXUatWLfr164ePjw8NGjTg6NGjwNkObdCgQeTn5zN16lS8vLyYMGECQ4YMweFw4Ofnx7PPPquAkwrTzfYiYlq6Dk5ETEsBJyKmpYATEdNSwImIaSngRMS0FHAiYloKOBExLQWciJjW/wPbDl8+P9xLKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing(tfidf_train,Y_tr,tfidf_test,Y_test,optimal_alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:09:09.662922Z",
     "start_time": "2019-01-27T00:08:48.732848Z"
    }
   },
   "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>Pos_Words</th>\n",
       "      <th>Pos_Importance</th>\n",
       "      <th>Neg_Words</th>\n",
       "      <th>Neg_Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>great</td>\n",
       "      <td>-6.297653</td>\n",
       "      <td>tast</td>\n",
       "      <td>-6.357245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>love</td>\n",
       "      <td>-6.299114</td>\n",
       "      <td>like</td>\n",
       "      <td>-6.490941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tast</td>\n",
       "      <td>-6.341551</td>\n",
       "      <td>product</td>\n",
       "      <td>-6.514279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>like</td>\n",
       "      <td>-6.351108</td>\n",
       "      <td>would</td>\n",
       "      <td>-6.837367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>good</td>\n",
       "      <td>-6.353745</td>\n",
       "      <td>coffe</td>\n",
       "      <td>-6.854715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tea</td>\n",
       "      <td>-6.395088</td>\n",
       "      <td>flavor</td>\n",
       "      <td>-6.874824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>flavor</td>\n",
       "      <td>-6.409692</td>\n",
       "      <td>one</td>\n",
       "      <td>-6.879084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>coffe</td>\n",
       "      <td>-6.422050</td>\n",
       "      <td>order</td>\n",
       "      <td>-6.990328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>use</td>\n",
       "      <td>-6.512606</td>\n",
       "      <td>buy</td>\n",
       "      <td>-6.993840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>product</td>\n",
       "      <td>-6.530451</td>\n",
       "      <td>tri</td>\n",
       "      <td>-6.994613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>one</td>\n",
       "      <td>-6.617949</td>\n",
       "      <td>box</td>\n",
       "      <td>-7.071098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tri</td>\n",
       "      <td>-6.673656</td>\n",
       "      <td>tea</td>\n",
       "      <td>-7.098207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>make</td>\n",
       "      <td>-6.756376</td>\n",
       "      <td>dont</td>\n",
       "      <td>-7.156228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>get</td>\n",
       "      <td>-6.819603</td>\n",
       "      <td>disappoint</td>\n",
       "      <td>-7.164726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>food</td>\n",
       "      <td>-6.855254</td>\n",
       "      <td>get</td>\n",
       "      <td>-7.175430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>price</td>\n",
       "      <td>-6.878565</td>\n",
       "      <td>good</td>\n",
       "      <td>-7.191501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>best</td>\n",
       "      <td>-6.886217</td>\n",
       "      <td>use</td>\n",
       "      <td>-7.211120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>buy</td>\n",
       "      <td>-6.890302</td>\n",
       "      <td>food</td>\n",
       "      <td>-7.245361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>order</td>\n",
       "      <td>-6.947551</td>\n",
       "      <td>even</td>\n",
       "      <td>-7.280433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>realli</td>\n",
       "      <td>-6.950182</td>\n",
       "      <td>bag</td>\n",
       "      <td>-7.323766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>time</td>\n",
       "      <td>-6.950935</td>\n",
       "      <td>bad</td>\n",
       "      <td>-7.332659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>find</td>\n",
       "      <td>-6.962161</td>\n",
       "      <td>purchas</td>\n",
       "      <td>-7.373697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>amazon</td>\n",
       "      <td>-6.962618</td>\n",
       "      <td>dog</td>\n",
       "      <td>-7.383235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>dog</td>\n",
       "      <td>-6.988113</td>\n",
       "      <td>packag</td>\n",
       "      <td>-7.385427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>eat</td>\n",
       "      <td>-6.997925</td>\n",
       "      <td>look</td>\n",
       "      <td>-7.387054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>would</td>\n",
       "      <td>-7.019121</td>\n",
       "      <td>bought</td>\n",
       "      <td>-7.410442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>store</td>\n",
       "      <td>-7.054939</td>\n",
       "      <td>amazon</td>\n",
       "      <td>-7.414227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>much</td>\n",
       "      <td>-7.090048</td>\n",
       "      <td>eat</td>\n",
       "      <td>-7.415962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>drink</td>\n",
       "      <td>-7.095435</td>\n",
       "      <td>much</td>\n",
       "      <td>-7.426037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>littl</td>\n",
       "      <td>-7.098959</td>\n",
       "      <td>tast like</td>\n",
       "      <td>-7.429794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700473</th>\n",
       "      <td>store germani</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion neapolitan</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700474</th>\n",
       "      <td>honest view</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion near</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700475</th>\n",
       "      <td>honest vendor</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion needless</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700476</th>\n",
       "      <td>rancid aftertast</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion neither</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700477</th>\n",
       "      <td>rancid almost</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion never</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700478</th>\n",
       "      <td>chewi border</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion newman</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700479</th>\n",
       "      <td>store ganular</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion newton</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700480</th>\n",
       "      <td>honest transpar</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion next</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700481</th>\n",
       "      <td>honest timothi</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion minor</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700482</th>\n",
       "      <td>watermelon give</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion milk</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700483</th>\n",
       "      <td>rancid ask</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion lipton</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700484</th>\n",
       "      <td>rancho call</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion method</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700485</th>\n",
       "      <td>chewi base</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion littl</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700486</th>\n",
       "      <td>chewi bare</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion long</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700487</th>\n",
       "      <td>chewi amazon</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion longer</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700488</th>\n",
       "      <td>worthwhil return</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion love</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700489</th>\n",
       "      <td>two went</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion lundberg</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700490</th>\n",
       "      <td>honesti cannot</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion lurpak</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700491</th>\n",
       "      <td>honesti best</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion magnesium</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700492</th>\n",
       "      <td>honesti believ</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion main</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700493</th>\n",
       "      <td>store gold</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion mani</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700494</th>\n",
       "      <td>honesti advertis</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion manner</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700495</th>\n",
       "      <td>rancho bowl</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion match</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700496</th>\n",
       "      <td>chewi arent</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion matcha</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700497</th>\n",
       "      <td>chewi asian</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion may</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700498</th>\n",
       "      <td>honest zero</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion mccann</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700499</th>\n",
       "      <td>honest wouldnt</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion mean</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700500</th>\n",
       "      <td>watermelon fruit</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion meat</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700501</th>\n",
       "      <td>honest worst</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>opinion melt</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700502</th>\n",
       "      <td>second seller</td>\n",
       "      <td>-16.819791</td>\n",
       "      <td>aaa</td>\n",
       "      <td>-15.477831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1700503 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pos_Words  Pos_Importance           Neg_Words  Neg_Importance\n",
       "0                   great       -6.297653                tast       -6.357245\n",
       "1                    love       -6.299114                like       -6.490941\n",
       "2                    tast       -6.341551             product       -6.514279\n",
       "3                    like       -6.351108               would       -6.837367\n",
       "4                    good       -6.353745               coffe       -6.854715\n",
       "5                     tea       -6.395088              flavor       -6.874824\n",
       "6                  flavor       -6.409692                 one       -6.879084\n",
       "7                   coffe       -6.422050               order       -6.990328\n",
       "8                     use       -6.512606                 buy       -6.993840\n",
       "9                 product       -6.530451                 tri       -6.994613\n",
       "10                    one       -6.617949                 box       -7.071098\n",
       "11                    tri       -6.673656                 tea       -7.098207\n",
       "12                   make       -6.756376                dont       -7.156228\n",
       "13                    get       -6.819603          disappoint       -7.164726\n",
       "14                   food       -6.855254                 get       -7.175430\n",
       "15                  price       -6.878565                good       -7.191501\n",
       "16                   best       -6.886217                 use       -7.211120\n",
       "17                    buy       -6.890302                food       -7.245361\n",
       "18                  order       -6.947551                even       -7.280433\n",
       "19                 realli       -6.950182                 bag       -7.323766\n",
       "20                   time       -6.950935                 bad       -7.332659\n",
       "21                   find       -6.962161             purchas       -7.373697\n",
       "22                 amazon       -6.962618                 dog       -7.383235\n",
       "23                    dog       -6.988113              packag       -7.385427\n",
       "24                    eat       -6.997925                look       -7.387054\n",
       "25                  would       -7.019121              bought       -7.410442\n",
       "26                  store       -7.054939              amazon       -7.414227\n",
       "27                   much       -7.090048                 eat       -7.415962\n",
       "28                  drink       -7.095435                much       -7.426037\n",
       "29                  littl       -7.098959           tast like       -7.429794\n",
       "...                   ...             ...                 ...             ...\n",
       "1700473     store germani      -16.819791  opinion neapolitan      -15.477831\n",
       "1700474       honest view      -16.819791        opinion near      -15.477831\n",
       "1700475     honest vendor      -16.819791    opinion needless      -15.477831\n",
       "1700476  rancid aftertast      -16.819791     opinion neither      -15.477831\n",
       "1700477     rancid almost      -16.819791       opinion never      -15.477831\n",
       "1700478      chewi border      -16.819791      opinion newman      -15.477831\n",
       "1700479     store ganular      -16.819791      opinion newton      -15.477831\n",
       "1700480   honest transpar      -16.819791        opinion next      -15.477831\n",
       "1700481    honest timothi      -16.819791       opinion minor      -15.477831\n",
       "1700482   watermelon give      -16.819791        opinion milk      -15.477831\n",
       "1700483        rancid ask      -16.819791      opinion lipton      -15.477831\n",
       "1700484       rancho call      -16.819791      opinion method      -15.477831\n",
       "1700485        chewi base      -16.819791       opinion littl      -15.477831\n",
       "1700486        chewi bare      -16.819791        opinion long      -15.477831\n",
       "1700487      chewi amazon      -16.819791      opinion longer      -15.477831\n",
       "1700488  worthwhil return      -16.819791        opinion love      -15.477831\n",
       "1700489          two went      -16.819791    opinion lundberg      -15.477831\n",
       "1700490    honesti cannot      -16.819791      opinion lurpak      -15.477831\n",
       "1700491      honesti best      -16.819791   opinion magnesium      -15.477831\n",
       "1700492    honesti believ      -16.819791        opinion main      -15.477831\n",
       "1700493        store gold      -16.819791        opinion mani      -15.477831\n",
       "1700494  honesti advertis      -16.819791      opinion manner      -15.477831\n",
       "1700495       rancho bowl      -16.819791       opinion match      -15.477831\n",
       "1700496       chewi arent      -16.819791      opinion matcha      -15.477831\n",
       "1700497       chewi asian      -16.819791         opinion may      -15.477831\n",
       "1700498       honest zero      -16.819791      opinion mccann      -15.477831\n",
       "1700499    honest wouldnt      -16.819791        opinion mean      -15.477831\n",
       "1700500  watermelon fruit      -16.819791        opinion meat      -15.477831\n",
       "1700501      honest worst      -16.819791        opinion melt      -15.477831\n",
       "1700502     second seller      -16.819791                 aaa      -15.477831\n",
       "\n",
       "[1700503 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnb = MultinomialNB(alpha = 0.05)\n",
    "mnb.fit(tfidf_train,Y_tr)\n",
    "imp_feature(tf_idf_vect,mnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-27T00:09:12.810988Z",
     "start_time": "2019-01-27T00:09:12.090947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "****Final Conclusion:****"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+---------------+-------------------------------+----------------+\n",
      "| Vectorizer |     Model     | Best hyper parameter($\u0007lpha$) | Test Auc Score |\n",
      "+------------+---------------+-------------------------------+----------------+\n",
      "|    BoW     | MultinomialNB |              0.1              |     93.56      |\n",
      "|   Tf-Idf   | MultinomialNB |              0.05             |     94.43      |\n",
      "+------------+---------------+-------------------------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "x = PrettyTable()\n",
    "x.field_names = [\"Vectorizer\", \"Model\", \"Best hyper parameter($\\alpha$)\",\"Test Auc Score\"]\n",
    "x.add_row([\"BoW\",\"MultinomialNB\",0.1,93.56])\n",
    "x.add_row([\"Tf-Idf\",\"MultinomialNB\",0.05,94.43])\n",
    "from IPython.display import Markdown, display\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "printmd('****Final Conclusion:****')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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