Yelp_NLP_LDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. pre-process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import codecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load datasets\n",
    "business = pd.read_csv(\"./yelp_dataset/business.csv\")\n",
    "user = pd.read_csv(\"./yelp_dataset/user.csv\")\n",
    "review = pd.read_csv(\"./yelp_dataset/review.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156639, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns and pre-process 'business' dataset\n",
    "business['business_avg_stars'] = business['stars']\n",
    "business = business[['business_id','name','neighborhood','city',\n",
    "                     'state','postal_code','latitude','longitude',\n",
    "                     'business_avg_stars','review_count','is_open','categories']]\n",
    "np.shape(business) #(156639, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1183362, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns and pre-process 'user' dataset\n",
    "user = user[['yelping_since','review_count','fans','average_stars','user_id']]\n",
    "np.shape(user) #(1183362, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4736897, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns and pre-process 'review' dataset\n",
    "np.shape(review) #(4736897, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4736897, 24)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge 3 datasets\n",
    "data = pd.merge(review, user, how='left', on ='user_id')\n",
    "data = pd.merge(data, business, how='left', on = 'business_id')\n",
    "np.shape(data) #(4736897, 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop NAs\n",
    "data = data.dropna(subset = ['stars'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add columns for NLP tags\n",
    "data['stars_dif'] = data['stars'] - data['average_stars']\n",
    "data['bus_stars_dif'] = data['stars'] - data['business_avg_stars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3952097, 26)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter 51 states\n",
    "data = data[data.state.isin([\n",
    "    \"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DC\", \"DE\", \"FL\", \n",
    "    \"GA\", \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \"KY\", \"LA\", \"ME\", \n",
    "    \"MD\", \"MA\", \"MI\", \"MN\", \"MS\", \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \n",
    "    \"NJ\", \"NM\", \"NY\", \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \n",
    "    \"SC\", \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"])]\n",
    "np.shape(data) #(3952097, 26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3505852, 26)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter still open\n",
    "data = data[data['is_open']==1]\n",
    "np.shape(data) #(3505852, 26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2043400, 26)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter 'Restaurant': our targets are restaurant business owners\n",
    "data = data[data['categories'].str.contains('Restaurants')]\n",
    "np.shape(data) #(2043400, 26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save the dataset\n",
    "data.to_csv('data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert int stars column to float\n",
    "data.stars = data.stars.astype(float)\n",
    "data.average_stars = data.average_stars.astype(float)\n",
    "[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. conduct sentiment analysis - NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,10,11,12,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2043401, 26)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funny</th>\n",
       "      <th>user_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>text</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>useful</th>\n",
       "      <th>cool</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>...</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>review_count_y</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>kzyLOqiJvyw_FWFTw2rjiQ</td>\n",
       "      <td>ByRzJ8rF2KJWLr-cUNU6EA</td>\n",
       "      <td>This place is horrible, we were so excited to ...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-06-17</td>\n",
       "      <td>...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85374.0</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>WZXp9-V2dqRRJqhGgRqueA</td>\n",
       "      <td>i5UwUPlQFPLcE8p2gPFwBw</td>\n",
       "      <td>For being fairly \"fast\" food.. Pei Wei (pronou...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015-03-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85374.0</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>XylT12exfdLiI_3uDLVIpw</td>\n",
       "      <td>EyQyvTTg2jX4or9bB8PC9g</td>\n",
       "      <td>I decided to try it out, I'm celiac and theref...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010-10-19</td>\n",
       "      <td>...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85374.0</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Ji9PeffxjwqPLO7pEfSpKQ</td>\n",
       "      <td>G-EFA005besj5uHsH0sQFA</td>\n",
       "      <td>I'm not saying Pei Wei is the best asian food ...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2009-01-12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2008-11-20</td>\n",
       "      <td>...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85374.0</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>TLIWzAJPrET0zX4_vgvLhg</td>\n",
       "      <td>6PcJSGUBSLjt4VLXos5C4A</td>\n",
       "      <td>Sometimes the food is spot on and delicious an...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-07-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-08-03</td>\n",
       "      <td>...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85374.0</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  funny                 user_id               review_id  \\\n",
       "0     0  kzyLOqiJvyw_FWFTw2rjiQ  ByRzJ8rF2KJWLr-cUNU6EA   \n",
       "1     0  WZXp9-V2dqRRJqhGgRqueA  i5UwUPlQFPLcE8p2gPFwBw   \n",
       "2     1  XylT12exfdLiI_3uDLVIpw  EyQyvTTg2jX4or9bB8PC9g   \n",
       "3     1  Ji9PeffxjwqPLO7pEfSpKQ  G-EFA005besj5uHsH0sQFA   \n",
       "4     0  TLIWzAJPrET0zX4_vgvLhg  6PcJSGUBSLjt4VLXos5C4A   \n",
       "\n",
       "                                                text             business_id  \\\n",
       "0  This place is horrible, we were so excited to ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "1  For being fairly \"fast\" food.. Pei Wei (pronou...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "2  I decided to try it out, I'm celiac and theref...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "3  I'm not saying Pei Wei is the best asian food ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "4  Sometimes the food is spot on and delicious an...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "\n",
       "   stars        date  useful  cool yelping_since      ...       state  \\\n",
       "0    1.0  2017-06-03     0.0   0.0    2016-06-17      ...          AZ   \n",
       "1    4.0  2015-03-26     1.0   0.0    2012-09-28      ...          AZ   \n",
       "2    5.0  2012-12-30     2.0   1.0    2010-10-19      ...          AZ   \n",
       "3    3.0  2009-01-12     1.0   1.0    2008-11-20      ...          AZ   \n",
       "4    3.0  2015-07-11     0.0   0.0    2014-08-03      ...          AZ   \n",
       "\n",
       "  postal_code   latitude   longitude business_avg_stars review_count_y  \\\n",
       "0     85374.0  33.638228 -112.365259                3.5           92.0   \n",
       "1     85374.0  33.638228 -112.365259                3.5           92.0   \n",
       "2     85374.0  33.638228 -112.365259                3.5           92.0   \n",
       "3     85374.0  33.638228 -112.365259                3.5           92.0   \n",
       "4     85374.0  33.638228 -112.365259                3.5           92.0   \n",
       "\n",
       "  is_open                                         categories  stars_dif  \\\n",
       "0     1.0  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -2.00   \n",
       "1     1.0  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...       0.46   \n",
       "2     1.0  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...       0.96   \n",
       "3     1.0  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -0.55   \n",
       "4     1.0  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -0.55   \n",
       "\n",
       "   bus_stars_dif  \n",
       "0           -2.5  \n",
       "1            0.5  \n",
       "2            1.5  \n",
       "3           -0.5  \n",
       "4           -0.5  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data.csv\", encoding='ISO-8859-1')\n",
    "print(np.shape(data)) #(2043400, 26)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "import nltk\n",
    "import nltk.classify.util\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize, RegexpTokenizer\n",
    "from nltk.classify import NaiveBayesClassifier\n",
    "from nltk.corpus import stopwords, names\n",
    "\n",
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def textPreProcess(reviews_column):\n",
    "    \n",
    "    filtered = []\n",
    "    temp = []\n",
    "    tokenizer = RegexpTokenizer(r'\\w+')\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "\n",
    "    for review in reviews_column:\n",
    "        words = tokenizer.tokenize(review)\n",
    "        for w in words:\n",
    "            if w.lower() not in stop_words:\n",
    "                temp.append(w.lower())\n",
    "        filtered.append(temp)\n",
    "        temp = []\n",
    "\n",
    "    return filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get pre-processed text data\n",
    "filtered = textPreProcess(data['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sentimentScore(data, filtered):\n",
    "    \n",
    "    neg_file = open(\"negative-words.txt\", \"r\", encoding='ISO-8859-1')\n",
    "    neg_dict = neg_file.read().split('\\n')\n",
    "    neg_file.close()\n",
    "\n",
    "    pos_file = open(\"positive-words.txt\", \"r\", encoding='ISO-8859-1')\n",
    "    pos_dict = pos_file.read().split('\\n')\n",
    "    pos_file.close() \n",
    "    \n",
    "    # University of Illinois at Chicago\n",
    "    # https://github.com/jeffreybreen/twitter-sentiment-analysis-tutorial-201107/tree/08a269765a6b185d5f3dd522c876043ba9628715/data/opinion-lexicon-English\n",
    "        \n",
    "    pos_counter = 0\n",
    "    neg_counter = 0\n",
    "    sent_ratio = 0\n",
    "    sentiment_score = []\n",
    "    \n",
    "    for review in filtered:\n",
    "        for word in review:\n",
    "            if word in pos_dict:\n",
    "                pos_counter = pos_counter + 1\n",
    "            if word in neg_dict:\n",
    "                neg_counter = neg_counter + 1\n",
    "                \n",
    "        if len(review) != 0:\n",
    "            sent_ratio = float(pos_counter - neg_counter) / float(len(review))  \n",
    "        else:\n",
    "            sent_ratio = 0\n",
    "        sentiment_score.append(sent_ratio)\n",
    "        pos_counter = 0\n",
    "        neg_counter = 0\n",
    "    \n",
    "    df = pd.DataFrame(np.column_stack([filtered, sentiment_score]), columns=['filtered_text', 'sentiment_score'])\n",
    "    \n",
    "    # reset indexes to zero so pd.concat works properly\n",
    "    data.index = (range(0, len(data)))\n",
    "    df.index = (range(0, len(df)))\n",
    "    return pd.concat([data, df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3h 54min 36s, sys: 3min 38s, total: 3h 58min 14s\n",
      "Wall time: 13h 41min 34s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "scored_df = sentimentScore(data, filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2043401, 28)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funny</th>\n",
       "      <th>user_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>text</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
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       "      <th>cool</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>...</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>review_count_y</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
       "      <th>filtered_text</th>\n",
       "      <th>sentiment_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>kzyLOqiJvyw_FWFTw2rjiQ</td>\n",
       "      <td>ByRzJ8rF2KJWLr-cUNU6EA</td>\n",
       "      <td>This place is horrible, we were so excited to ...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-06-17</td>\n",
       "      <td>...</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>[place, horrible, excited, try, since, got, gi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>WZXp9-V2dqRRJqhGgRqueA</td>\n",
       "      <td>i5UwUPlQFPLcE8p2gPFwBw</td>\n",
       "      <td>For being fairly \"fast\" food.. Pei Wei (pronou...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015-03-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>[fairly, fast, food, pei, wei, pronounced, pay...</td>\n",
       "      <td>0.375</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>XylT12exfdLiI_3uDLVIpw</td>\n",
       "      <td>EyQyvTTg2jX4or9bB8PC9g</td>\n",
       "      <td>I decided to try it out, I'm celiac and theref...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010-10-19</td>\n",
       "      <td>...</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.5</td>\n",
       "      <td>[decided, try, celiac, therefore, eat, gluten,...</td>\n",
       "      <td>0.0757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Ji9PeffxjwqPLO7pEfSpKQ</td>\n",
       "      <td>G-EFA005besj5uHsH0sQFA</td>\n",
       "      <td>I'm not saying Pei Wei is the best asian food ...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2009-01-12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2008-11-20</td>\n",
       "      <td>...</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>[saying, pei, wei, best, asian, food, ever, ta...</td>\n",
       "      <td>0.0898204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>TLIWzAJPrET0zX4_vgvLhg</td>\n",
       "      <td>6PcJSGUBSLjt4VLXos5C4A</td>\n",
       "      <td>Sometimes the food is spot on and delicious an...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-07-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-08-03</td>\n",
       "      <td>...</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>[sometimes, food, spot, delicious, times, quit...</td>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  funny                 user_id               review_id  \\\n",
       "0     0  kzyLOqiJvyw_FWFTw2rjiQ  ByRzJ8rF2KJWLr-cUNU6EA   \n",
       "1     0  WZXp9-V2dqRRJqhGgRqueA  i5UwUPlQFPLcE8p2gPFwBw   \n",
       "2     1  XylT12exfdLiI_3uDLVIpw  EyQyvTTg2jX4or9bB8PC9g   \n",
       "3     1  Ji9PeffxjwqPLO7pEfSpKQ  G-EFA005besj5uHsH0sQFA   \n",
       "4     0  TLIWzAJPrET0zX4_vgvLhg  6PcJSGUBSLjt4VLXos5C4A   \n",
       "\n",
       "                                                text             business_id  \\\n",
       "0  This place is horrible, we were so excited to ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "1  For being fairly \"fast\" food.. Pei Wei (pronou...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "2  I decided to try it out, I'm celiac and theref...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "3  I'm not saying Pei Wei is the best asian food ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "4  Sometimes the food is spot on and delicious an...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "\n",
       "   stars        date  useful  cool yelping_since       ...          latitude  \\\n",
       "0    1.0  2017-06-03     0.0   0.0    2016-06-17       ...         33.638228   \n",
       "1    4.0  2015-03-26     1.0   0.0    2012-09-28       ...         33.638228   \n",
       "2    5.0  2012-12-30     2.0   1.0    2010-10-19       ...         33.638228   \n",
       "3    3.0  2009-01-12     1.0   1.0    2008-11-20       ...         33.638228   \n",
       "4    3.0  2015-07-11     0.0   0.0    2014-08-03       ...         33.638228   \n",
       "\n",
       "    longitude business_avg_stars review_count_y is_open  \\\n",
       "0 -112.365259                3.5           92.0     1.0   \n",
       "1 -112.365259                3.5           92.0     1.0   \n",
       "2 -112.365259                3.5           92.0     1.0   \n",
       "3 -112.365259                3.5           92.0     1.0   \n",
       "4 -112.365259                3.5           92.0     1.0   \n",
       "\n",
       "                                          categories stars_dif  bus_stars_dif  \\\n",
       "0  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -2.00           -2.5   \n",
       "1  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      0.46            0.5   \n",
       "2  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      0.96            1.5   \n",
       "3  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -0.55           -0.5   \n",
       "4  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -0.55           -0.5   \n",
       "\n",
       "                                       filtered_text  sentiment_score  \n",
       "0  [place, horrible, excited, try, since, got, gi...                0  \n",
       "1  [fairly, fast, food, pei, wei, pronounced, pay...            0.375  \n",
       "2  [decided, try, celiac, therefore, eat, gluten,...        0.0757576  \n",
       "3  [saying, pei, wei, best, asian, food, ever, ta...        0.0898204  \n",
       "4  [sometimes, food, spot, delicious, times, quit...         0.117647  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored_df.to_csv('scored_df.csv', index=False)\n",
    "print(np.shape(scored_df))\n",
    "scored_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(223486, 28)\n"
     ]
    },
    {
     "data": {
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       "      <th>sentiment_score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>15</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2015-09-22</td>\n",
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       "      <td>-1.5</td>\n",
       "      <td>[disappointed, yelp, hours, show, open, 1030am...</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>alTlRb9qMBX11pARX05Big</td>\n",
       "      <td>jJaU6pbKkYgl15P-5zfg3A</td>\n",
       "      <td>Everything was good except Thai chicken wraps....</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2014-05-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010-09-17</td>\n",
       "      <td>...</td>\n",
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       "      <td>3.5</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>[everything, good, except, thai, chicken, wrap...</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>R-kL1bocHgP4GW7Mgd-ZXA</td>\n",
       "      <td>nI2rhDM2CgNazMdhiODoRQ</td>\n",
       "      <td>OH MY GOD, this place. I used to like Pei Wei ...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-02-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-12-04</td>\n",
       "      <td>...</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>[oh, god, place, used, like, pei, wei, moved, ...</td>\n",
       "      <td>-0.0229885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>mCE5jUDzP-ihU_6cwQsiiw</td>\n",
       "      <td>FxAxYxj5SbiOtcK36bJxCA</td>\n",
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       "      <td>2011-02-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-02-26</td>\n",
       "      <td>...</td>\n",
       "      <td>33.638228</td>\n",
       "      <td>-112.365259</td>\n",
       "      <td>3.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>[went, family, 6, treated, poorly, manager, pl...</td>\n",
       "      <td>-0.0576923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   funny                 user_id               review_id  \\\n",
       "15     0  ujOPJEz_KxzAyZDnji-2Ng  QXWku_OB3FCj9VCZfUZwwg   \n",
       "16     0  6aEUn50d3Ts7MiGu6WdpKA  5NtaW5EwXK595kP4Ynnisg   \n",
       "29     1  alTlRb9qMBX11pARX05Big  jJaU6pbKkYgl15P-5zfg3A   \n",
       "33     0  R-kL1bocHgP4GW7Mgd-ZXA  nI2rhDM2CgNazMdhiODoRQ   \n",
       "44     0  mCE5jUDzP-ihU_6cwQsiiw  FxAxYxj5SbiOtcK36bJxCA   \n",
       "\n",
       "                                                 text             business_id  \\\n",
       "15  I wish I could give 1.5 stars. Nothing special...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "16  Disappointed that on yelp their hours show the...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "29  Everything was good except Thai chicken wraps....  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "33  OH MY GOD, this place. I used to like Pei Wei ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "44  We went as a family of 6 and was treated very ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "\n",
       "    stars        date  useful  cool yelping_since       ...          latitude  \\\n",
       "15    1.0  2017-07-08     0.0   0.0    2017-07-01       ...         33.638228   \n",
       "16    2.0  2015-09-22     1.0   0.0    2013-06-27       ...         33.638228   \n",
       "29    3.0  2014-05-29     0.0   1.0    2010-09-17       ...         33.638228   \n",
       "33    2.0  2016-02-14     0.0   0.0    2010-12-04       ...         33.638228   \n",
       "44    1.0  2011-02-27     0.0   0.0    2011-02-26       ...         33.638228   \n",
       "\n",
       "     longitude business_avg_stars review_count_y is_open  \\\n",
       "15 -112.365259                3.5           92.0     1.0   \n",
       "16 -112.365259                3.5           92.0     1.0   \n",
       "29 -112.365259                3.5           92.0     1.0   \n",
       "33 -112.365259                3.5           92.0     1.0   \n",
       "44 -112.365259                3.5           92.0     1.0   \n",
       "\n",
       "                                           categories stars_dif  \\\n",
       "15  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -2.67   \n",
       "16  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -2.13   \n",
       "29  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -0.48   \n",
       "33  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -1.63   \n",
       "44  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...     -1.21   \n",
       "\n",
       "    bus_stars_dif                                      filtered_text  \\\n",
       "15           -2.5  [wish, could, give, 1, 5, stars, nothing, spec...   \n",
       "16           -1.5  [disappointed, yelp, hours, show, open, 1030am...   \n",
       "29           -0.5  [everything, good, except, thai, chicken, wrap...   \n",
       "33           -1.5  [oh, god, place, used, like, pei, wei, moved, ...   \n",
       "44           -2.5  [went, family, 6, treated, poorly, manager, pl...   \n",
       "\n",
       "    sentiment_score  \n",
       "15       -0.0333333  \n",
       "16            -0.04  \n",
       "29            -0.04  \n",
       "33       -0.0229885  \n",
       "44       -0.0576923  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the neg_scored dataframe for analysis\n",
    "#scored_df = pd.read_csv(\"scored_df.csv\", encoding='ISO-8859-1')\n",
    "neg_scores = scored_df[scored_df['sentiment_score'] < 0]\n",
    "print(np.shape(neg_scores))\n",
    "neg_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neg_scores.to_csv('neg_scores.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. EDA and statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,10,11,12,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2043401, 28)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scored_df\n",
    "scored_df = pd.read_csv(\"scored_df.csv\", encoding='ISO-8859-1')\n",
    "np.shape(scored_df) #(2043401, 28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stars</th>\n",
       "      <th>useful</th>\n",
       "      <th>cool</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>review_count_y</th>\n",
       "      <th>is_open</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
       "      <th>sentiment_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.043400e+06</td>\n",
       "      <td>2.043400e+06</td>\n",
       "      <td>2.043400e+06</td>\n",
       "      <td>2.042975e+06</td>\n",
       "      <td>2.043400e+06</td>\n",
       "      <td>2.043400e+06</td>\n",
       "      <td>2.043400e+06</td>\n",
       "      <td>2043400.0</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.043401e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.748967e+00</td>\n",
       "      <td>9.164495e-01</td>\n",
       "      <td>5.244377e-01</td>\n",
       "      <td>7.397929e+04</td>\n",
       "      <td>3.598703e+01</td>\n",
       "      <td>-1.058747e+02</td>\n",
       "      <td>5.253169e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.154558e-03</td>\n",
       "      <td>-2.966381e-03</td>\n",
       "      <td>1.053572e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.358270e+00</td>\n",
       "      <td>2.498608e+00</td>\n",
       "      <td>2.101432e+00</td>\n",
       "      <td>2.428915e+04</td>\n",
       "      <td>2.731777e+00</td>\n",
       "      <td>1.377946e+01</td>\n",
       "      <td>8.720525e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.166744e+00</td>\n",
       "      <td>1.243536e+00</td>\n",
       "      <td>1.074904e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>3.313739e+01</td>\n",
       "      <td>-1.156796e+02</td>\n",
       "      <td>3.500000e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.910000e+00</td>\n",
       "      <td>-4.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.500300e+04</td>\n",
       "      <td>3.353931e+01</td>\n",
       "      <td>-1.151702e+02</td>\n",
       "      <td>1.030000e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-6.700000e-01</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>3.773585e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.526000e+04</td>\n",
       "      <td>3.606815e+01</td>\n",
       "      <td>-1.120655e+02</td>\n",
       "      <td>2.480000e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.100000e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>9.722222e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.910900e+04</td>\n",
       "      <td>3.614495e+01</td>\n",
       "      <td>-1.116799e+02</td>\n",
       "      <td>5.610000e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.200000e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.666667e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>2.510000e+02</td>\n",
       "      <td>2.310000e+02</td>\n",
       "      <td>9.802300e+04</td>\n",
       "      <td>8.830000e+02</td>\n",
       "      <td>1.409264e+01</td>\n",
       "      <td>6.979000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.850000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              stars        useful          cool   postal_code      latitude  \\\n",
       "count  2.043400e+06  2.043400e+06  2.043400e+06  2.042975e+06  2.043400e+06   \n",
       "mean   3.748967e+00  9.164495e-01  5.244377e-01  7.397929e+04  3.598703e+01   \n",
       "std    1.358270e+00  2.498608e+00  2.101432e+00  2.428915e+04  2.731777e+00   \n",
       "min    1.000000e+00  0.000000e+00  0.000000e+00  4.000000e+00  3.313739e+01   \n",
       "25%    3.000000e+00  0.000000e+00  0.000000e+00  8.500300e+04  3.353931e+01   \n",
       "50%    4.000000e+00  0.000000e+00  0.000000e+00  8.526000e+04  3.606815e+01   \n",
       "75%    5.000000e+00  1.000000e+00  0.000000e+00  8.910900e+04  3.614495e+01   \n",
       "max    5.000000e+00  2.510000e+02  2.310000e+02  9.802300e+04  8.830000e+02   \n",
       "\n",
       "          longitude  review_count_y    is_open     stars_dif  bus_stars_dif  \\\n",
       "count  2.043400e+06    2.043400e+06  2043400.0  2.043399e+06   2.043399e+06   \n",
       "mean  -1.058747e+02    5.253169e+02        1.0 -3.154558e-03  -2.966381e-03   \n",
       "std    1.377946e+01    8.720525e+02        0.0  1.166744e+00   1.243536e+00   \n",
       "min   -1.156796e+02    3.500000e-01        1.0 -3.910000e+00  -4.000000e+00   \n",
       "25%   -1.151702e+02    1.030000e+02        1.0 -6.700000e-01  -1.000000e+00   \n",
       "50%   -1.120655e+02    2.480000e+02        1.0  1.100000e-01   5.000000e-01   \n",
       "75%   -1.116799e+02    5.610000e+02        1.0  8.200000e-01   1.000000e+00   \n",
       "max    1.409264e+01    6.979000e+03        1.0  3.850000e+00   4.000000e+00   \n",
       "\n",
       "       sentiment_score  \n",
       "count     2.043401e+06  \n",
       "mean      1.053572e-01  \n",
       "std       1.074904e-01  \n",
       "min      -1.000000e+00  \n",
       "25%       3.773585e-02  \n",
       "50%       9.722222e-02  \n",
       "75%       1.666667e-01  \n",
       "max       1.000000e+00  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funny</th>\n",
       "      <th>user_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>text</th>\n",
       "      <th>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>useful</th>\n",
       "      <th>cool</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>review_count_x</th>\n",
       "      <th>...</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>review_count_y</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
       "      <th>filtered_text</th>\n",
       "      <th>sentiment_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stars</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>...</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228345</td>\n",
       "      <td>228345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>...</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "      <td>190147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>...</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "      <td>265286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>...</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "      <td>541968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>...</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "      <td>817654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        funny  user_id  review_id    text  business_id    date  useful  \\\n",
       "stars                                                                    \n",
       "1.0    228345   228345     228345  228345       228345  228345  228345   \n",
       "2.0    190147   190147     190147  190147       190147  190147  190147   \n",
       "3.0    265286   265286     265286  265286       265286  265286  265286   \n",
       "4.0    541968   541968     541968  541968       541968  541968  541968   \n",
       "5.0    817654   817654     817654  817654       817654  817654  817654   \n",
       "\n",
       "         cool  yelping_since  review_count_x       ...         latitude  \\\n",
       "stars                                              ...                    \n",
       "1.0    228345         228345          228345       ...           228345   \n",
       "2.0    190147         190147          190147       ...           190147   \n",
       "3.0    265286         265286          265286       ...           265286   \n",
       "4.0    541968         541968          541968       ...           541968   \n",
       "5.0    817654         817654          817654       ...           817654   \n",
       "\n",
       "       longitude  business_avg_stars  review_count_y  is_open  categories  \\\n",
       "stars                                                                       \n",
       "1.0       228345              228345          228345   228345      228344   \n",
       "2.0       190147              190147          190147   190147      190147   \n",
       "3.0       265286              265286          265286   265286      265286   \n",
       "4.0       541968              541968          541968   541968      541968   \n",
       "5.0       817654              817654          817654   817654      817654   \n",
       "\n",
       "       stars_dif  bus_stars_dif  filtered_text  sentiment_score  \n",
       "stars                                                            \n",
       "1.0       228344         228344         228345           228345  \n",
       "2.0       190147         190147         190147           190147  \n",
       "3.0       265286         265286         265286           265286  \n",
       "4.0       541968         541968         541968           541968  \n",
       "5.0       817654         817654         817654           817654  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored_df.groupby('stars').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.567849078131\n",
      "0.454064083615\n"
     ]
    }
   ],
   "source": [
    "print(scored_df['stars'].corr(scored_df['sentiment_score']))\n",
    "print(scored_df['stars_dif'].corr(scored_df['sentiment_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stars</th>\n",
       "      <th>useful</th>\n",
       "      <th>cool</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>review_count_y</th>\n",
       "      <th>is_open</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>223486.000000</td>\n",
       "      <td>223486.000000</td>\n",
       "      <td>223486.000000</td>\n",
       "      <td>223442.000000</td>\n",
       "      <td>223486.000000</td>\n",
       "      <td>223486.000000</td>\n",
       "      <td>223486.000000</td>\n",
       "      <td>223486.0</td>\n",
       "      <td>223486.000000</td>\n",
       "      <td>223486.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.863678</td>\n",
       "      <td>0.961179</td>\n",
       "      <td>0.308458</td>\n",
       "      <td>74168.084304</td>\n",
       "      <td>36.005578</td>\n",
       "      <td>-105.948270</td>\n",
       "      <td>432.918205</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.292165</td>\n",
       "      <td>-1.506472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.166737</td>\n",
       "      <td>2.203883</td>\n",
       "      <td>1.449254</td>\n",
       "      <td>24142.503419</td>\n",
       "      <td>2.647370</td>\n",
       "      <td>13.769752</td>\n",
       "      <td>790.379761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.164322</td>\n",
       "      <td>1.164728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2224.000000</td>\n",
       "      <td>33.137390</td>\n",
       "      <td>-115.644741</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.890000</td>\n",
       "      <td>-4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85003.000000</td>\n",
       "      <td>33.567825</td>\n",
       "      <td>-115.170430</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.190000</td>\n",
       "      <td>-2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85281.000000</td>\n",
       "      <td>36.072370</td>\n",
       "      <td>-112.073929</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.440000</td>\n",
       "      <td>-1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>89109.000000</td>\n",
       "      <td>36.148619</td>\n",
       "      <td>-111.682424</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.360000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>98023.000000</td>\n",
       "      <td>44.992140</td>\n",
       "      <td>-73.363621</td>\n",
       "      <td>6979.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               stars         useful           cool    postal_code  \\\n",
       "count  223486.000000  223486.000000  223486.000000  223442.000000   \n",
       "mean        1.863678       0.961179       0.308458   74168.084304   \n",
       "std         1.166737       2.203883       1.449254   24142.503419   \n",
       "min         1.000000       0.000000       0.000000    2224.000000   \n",
       "25%         1.000000       0.000000       0.000000   85003.000000   \n",
       "50%         1.000000       0.000000       0.000000   85281.000000   \n",
       "75%         2.000000       1.000000       0.000000   89109.000000   \n",
       "max         5.000000     251.000000     168.000000   98023.000000   \n",
       "\n",
       "            latitude      longitude  review_count_y   is_open      stars_dif  \\\n",
       "count  223486.000000  223486.000000   223486.000000  223486.0  223486.000000   \n",
       "mean       36.005578    -105.948270      432.918205       1.0      -1.292165   \n",
       "std         2.647370      13.769752      790.379761       0.0       1.164322   \n",
       "min        33.137390    -115.644741        3.000000       1.0      -3.890000   \n",
       "25%        33.567825    -115.170430       67.000000       1.0      -2.190000   \n",
       "50%        36.072370    -112.073929      178.000000       1.0      -1.440000   \n",
       "75%        36.148619    -111.682424      435.000000       1.0      -0.360000   \n",
       "max        44.992140     -73.363621     6979.000000       1.0       3.560000   \n",
       "\n",
       "       bus_stars_dif  \n",
       "count  223486.000000  \n",
       "mean       -1.506472  \n",
       "std         1.164728  \n",
       "min        -4.000000  \n",
       "25%        -2.500000  \n",
       "50%        -1.500000  \n",
       "75%        -1.000000  \n",
       "max         3.500000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# neg_scores\n",
    "neg_scores.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funny</th>\n",
       "      <th>user_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>text</th>\n",
       "      <th>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>useful</th>\n",
       "      <th>cool</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>review_count_x</th>\n",
       "      <th>...</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>review_count_y</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
       "      <th>filtered_text</th>\n",
       "      <th>sentiment_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stars</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>...</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "      <td>120176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>...</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "      <td>51259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>...</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "      <td>25979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>...</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "      <td>14485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>...</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        funny  user_id  review_id    text  business_id    date  useful  \\\n",
       "stars                                                                    \n",
       "1.0    120176   120176     120176  120176       120176  120176  120176   \n",
       "2.0     51259    51259      51259   51259        51259   51259   51259   \n",
       "3.0     25979    25979      25979   25979        25979   25979   25979   \n",
       "4.0     14485    14485      14485   14485        14485   14485   14485   \n",
       "5.0     11587    11587      11587   11587        11587   11587   11587   \n",
       "\n",
       "         cool  yelping_since  review_count_x       ...         latitude  \\\n",
       "stars                                              ...                    \n",
       "1.0    120176         120176          120176       ...           120176   \n",
       "2.0     51259          51259           51259       ...            51259   \n",
       "3.0     25979          25979           25979       ...            25979   \n",
       "4.0     14485          14485           14485       ...            14485   \n",
       "5.0     11587          11587           11587       ...            11587   \n",
       "\n",
       "       longitude  business_avg_stars  review_count_y  is_open  categories  \\\n",
       "stars                                                                       \n",
       "1.0       120176              120176          120176   120176      120176   \n",
       "2.0        51259               51259           51259    51259       51259   \n",
       "3.0        25979               25979           25979    25979       25979   \n",
       "4.0        14485               14485           14485    14485       14485   \n",
       "5.0        11587               11587           11587    11587       11587   \n",
       "\n",
       "       stars_dif  bus_stars_dif  filtered_text  sentiment_score  \n",
       "stars                                                            \n",
       "1.0       120176         120176         120176           120176  \n",
       "2.0        51259          51259          51259            51259  \n",
       "3.0        25979          25979          25979            25979  \n",
       "4.0        14485          14485          14485            14485  \n",
       "5.0        11587          11587          11587            11587  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_scores.groupby('stars').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.14817384826249083, pvalue=0.0)\n",
      "SpearmanrResult(correlation=0.046836975321569628, pvalue=9.5692159238598414e-109)\n"
     ]
    }
   ],
   "source": [
    "import scipy as sp\n",
    "print(sp.stats.spearmanr(neg_scores['stars'], neg_scores['sentiment_score']))\n",
    "print(sp.stats.spearmanr(neg_scores['stars_dif'], neg_scores['sentiment_score']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- distribution of sentiment score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.4, 0.4)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KMIvxTB/GMr0YzytNHO6SJG1YVXXjY3NmQKFgIANVFSbGMr1mvp7v3bNST75+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\nUm1Y/EqSJKk2LH4lSZJUGxa/kiRJqo3fJxg7iMzeSfkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4dfe4ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.distplot(scored_df['sentiment_score'], label='Sentiment Score')\n",
    "sns.plt.xlim(-0.4,0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- overall / negative review count "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4d03cfdd8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4dfe459e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_df = scored_df.groupby('stars')['is_open'].sum()\n",
    "bar_df.plot.bar()\n",
    "# fig, ax = plt.subplots()\n",
    "# ax2 = ax.twiny() # put a second plot on top of it, having the same y axis\n",
    "# data1 = np.random.random(10) #random in [0,1]\n",
    "# data2 = np.random.random(15)+1 #random in [1,2]\n",
    "\n",
    "# ax.plot(np.arange(len(data1)), data1) # plot in the first plot\n",
    "# ax2.plot(np.arange(len(data2)),data2) # plot in the second plot\n",
    "\n",
    "# https://stackoverflow.com/questions/16748577/matplotlib-combine-different-figures-and-put-them-in-a-single-subplot-sharing-a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x405270d30>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZwEMHKmTRovkMDc1puIz+MTIy3HYJfcNedMykXsyktfSqn3vRKBQy80cAETFMFQ5rgI9m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x40529a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_df = neg_scores.groupby('stars')['is_open'].sum()\n",
    "bar_df.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- overall sentiment_score and stars_dif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distplot3 = scored_df.loc[:,['sentiment_score', 'stars_dif', 'stars']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4, 2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4052a59e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SkpNQXtqxP9TlLvx+ygOk8c0Jk+J0cNiazvNHROQLQRCgrGv5hYIx0QChtjokx+4sBFFs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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4052a5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "sns.jointplot(distplot3.stars_dif, distplot3.sentiment_score, kind = 'kde', cmap=\"Reds\", shade=True, shade_lowest=False)\n",
    "#sns.plt.ylim(-0.25,0)\n",
    "sns.plt.xlim((-4),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- focus on negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distplot = neg_scores.loc[:,['sentiment_score', 'stars_dif']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4, 2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4d11f37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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o/hUgAI+uXI2NW/6HFLMZWq0WbW3tfusaPWIoFj21Es+ufQtujwf33vozjBox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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4d11f34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "sns.jointplot(distplot.stars_dif, distplot.sentiment_score, kind = 'kde', cmap=\"Reds\", shade=True, shade_lowest=False)\n",
    "sns.plt.ylim(-0.25,0)\n",
    "sns.plt.xlim((-4),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- stars_dif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4d11f9208>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MsDkZZQHAlEcIBgqsvTes3Uf7lUgSTjBxQ5Gkykoy915nOQQAXCrjjXEA8sOyLB1uG9K+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\nE0xEREREQec/sufyVanbCdEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4d11f9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.distplot(neg_scores['stars_dif'], label='Stars_Dif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Phrase modeling, LDA - spacy, gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import Phrases\n",
    "from gensim.corpora import Dictionary, MmCorpus\n",
    "from gensim.models.word2vec import LineSentence\n",
    "from gensim.models.ldamulticore import LdaMulticore\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "import warnings\n",
    "import _pickle as pickle\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# helper functions for text preprocessing & LDA modeling:\n",
    "\n",
    "def punct_space(token):\n",
    "    \"\"\"\n",
    "    helper function to eliminate tokens\n",
    "    that are pure punctuation or whitespace\n",
    "    \"\"\"\n",
    "    \n",
    "    return token.is_punct or token.is_space or token.like_num or token.is_digit\n",
    "\n",
    "def line_review(filename):\n",
    "    \"\"\"\n",
    "    generator function to read in reviews from Pandas Series\n",
    "    and un-escape the original line breaks in the text\n",
    "    \"\"\"\n",
    "    \n",
    "    #with codecs.open(filename, encoding='utf_8') as f:\n",
    "    for review in filename:\n",
    "        yield review.replace('\\\\n', '\\n')\n",
    "            \n",
    "def lemmatized_sentence_corpus(filename):\n",
    "    \"\"\"\n",
    "    generator function to use spaCy to parse reviews,\n",
    "    lemmatize the text, and yield sentences\n",
    "    \"\"\"\n",
    "    \n",
    "    for parsed_review in nlp.pipe(line_review(filename), batch_size=10000, n_threads=4):\n",
    "        \n",
    "        for sent in parsed_review.sents:\n",
    "            yield u' '.join([token.lemma_ for token in sent\n",
    "                             if not punct_space(token)])\n",
    "\n",
    "def trigram_bow_generator(filepath):\n",
    "    \"\"\"\n",
    "    generator function to read reviews from a file\n",
    "    and yield a bag-of-words representation\n",
    "    \"\"\"\n",
    "    # load finished dictionary from disk\n",
    "    trigram_dictionary = Dictionary.load('./models/trigram_dict_all_neg.dict')\n",
    "    \n",
    "    for review in LineSentence(filepath):\n",
    "        yield trigram_dictionary.doc2bow(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reviewPreProcess(text):\n",
    "    \"\"\"\n",
    "    Takes Pandas series as input,\n",
    "    consisting of one review as \n",
    "    text string per row\n",
    "    \"\"\"\n",
    "    \n",
    "    # lemmatized_sentence_corpus generator loops over original review text, segments the reviews into individual sentences \n",
    "    # and normalizes text. Writes data to new file with one normalized sentence per line:\n",
    "    with codecs.open('./models/unigram_sentences_neg.txt', 'w', encoding='utf_8') as f:\n",
    "        for sentence in lemmatized_sentence_corpus(text):  \n",
    "            f.write(sentence + '\\n')\n",
    "    \n",
    "    # Create object to stream unigram sentences from disk, rather than hold in memory:\n",
    "    unigram_sentences = LineSentence('./models/unigram_sentences_neg.txt')\n",
    "        \n",
    "    # Train phrase model to link individual words into two-word phrases: \n",
    "    bigram_model = Phrases(unigram_sentences)\n",
    "    bigram_model.save('./models/bigram_model_neg.txt')\n",
    "            \n",
    "    # Apply trained bigram phrase model to the review sentences data: \n",
    "    with codecs.open('./models/bigram_sentences_neg.txt', 'w', encoding='utf_8') as f:\n",
    "        for unigram_sentence in unigram_sentences:\n",
    "            bigram_sentence = u' '.join(bigram_model[unigram_sentence])\n",
    "            f.write(bigram_sentence + '\\n')\n",
    "    \n",
    "    # Create object to stream bigram sentences from disk, rather than hold in memory:\n",
    "    bigram_sentences = LineSentence('./models/bigram_sentences_neg.txt')\n",
    "    \n",
    "    # Train second-order phrase model to to generate trigrams:\n",
    "    trigram_model = Phrases(bigram_sentences)\n",
    "    trigram_model.save('./models/trigram_model_neg.txt')\n",
    "    \n",
    "    # Apply trained second-order phrase model to our first-order transformed sentences and write to a new file:\n",
    "    #with codecs.open('./models/trigram_sentences_neg.txt', 'w', encoding='utf_8') as f:\n",
    "    #    for bigram_sentence in bigram_sentences:\n",
    "    #        trigram_sentence = u' '.join(trigram_model[bigram_sentence])\n",
    "    #        f.write(trigram_sentence + '\\n')\n",
    "    \n",
    "    # Run complete text of the reviews through a pipeline that applies text normalization and phrase models.\n",
    "    # Also remove stopwords and write transformed text to a new file, one review per line:\n",
    "    with codecs.open('./models/trigram_transformed_reviews_neg.txt', 'w', encoding='utf_8') as f:\n",
    "        for parsed_review in nlp.pipe(line_review(text), \n",
    "                                        batch_size=10000, n_threads=4):\n",
    "            \n",
    "            # lemmatize the text, removing punctuation and whitespace\n",
    "            unigram_review = [token.lemma_ for token in parsed_review\n",
    "                                if not punct_space(token)]\n",
    "            \n",
    "            # apply the first-order and second-order phrase models\n",
    "            bigram_review = bigram_model[unigram_review]\n",
    "            trigram_review = trigram_model[bigram_review]\n",
    "                \n",
    "            common_terms = ['order', 'come', 'bad', 'good', \\\n",
    "                            'place', 'time', '\\'s'] \n",
    "                \n",
    "            # remove any remaining stopwords\n",
    "            trigram_review = [term for term in trigram_review\n",
    "                                if term not in spacy.en.English.Defaults.stop_words]\n",
    "            trigram_review = [term for term in trigram_review\n",
    "                                if term not in common_terms]\n",
    "            \n",
    "            # write the transformed review as a line in the new file\n",
    "            trigram_review = u' '.join(trigram_review)\n",
    "            f.write(trigram_review + '\\n')\n",
    "    \n",
    "    \n",
    "    # Learn full vocabulary of corpus to be modeled, using gensim's Dictionary class.  \n",
    "    # Stream reviews off of disk using LineSentence:\n",
    "    trigram_reviews = LineSentence('./models/trigram_transformed_reviews_neg.txt')\n",
    "\n",
    "    # learn the dictionary by iterating over all of the reviews\n",
    "    trigram_dictionary = Dictionary(trigram_reviews)\n",
    "    \n",
    "    # filter tokens that are very rare or too common from\n",
    "    # the dictionary (filter_extremes) and reassign integer ids (compactify)\n",
    "    trigram_dictionary.filter_extremes(no_below=10, no_above=0.4)\n",
    "    trigram_dictionary.compactify()\n",
    "\n",
    "    trigram_dictionary.save('./models/trigram_dict_all_neg.dict')\n",
    "    \n",
    "    return bigram_model, trigram_model, trigram_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def LDA_Model(topics, cores=3):\n",
    "    \"\"\"\n",
    "    Topics represents desired LDA topics,\n",
    "    cores should be physical cores minus one. \n",
    "    Both should be integers.\n",
    "    \"\"\"\n",
    "    \n",
    "    # load finished dictionary from disk\n",
    "    trigram_dictionary = Dictionary.load('./models/trigram_dict_all_neg.dict')\n",
    "        \n",
    "    # generate bag-of-words representations for all reviews and save them as a matrix\n",
    "    MmCorpus.serialize('./models/trigram_bow_corpus_neg.nm',\n",
    "                        trigram_bow_generator('./models/trigram_transformed_reviews_neg.txt'))\n",
    "    \n",
    "    # load finished bag-of-words corpus from disk\n",
    "    trigram_bow_corpus = MmCorpus('./models/trigram_bow_corpus_neg.nm')\n",
    "    \n",
    "    \n",
    "    # Pass the bag-of-words matrix and Dictionary from previous steps to LdaMulticore as inputs, \n",
    "    # along with the number of topics the model should learn\n",
    "        \n",
    "    # workers => sets the parallelism, and should be\n",
    "    # set to your number of physical cores minus one\n",
    "    lda = LdaMulticore(trigram_bow_corpus, \n",
    "                       num_topics=topics, \n",
    "                       id2word=trigram_dictionary, \n",
    "                       workers=cores)\n",
    "        \n",
    "    lda.save('./models/lda_model_neg')\n",
    "        \n",
    "    # load the finished LDA model from disk\n",
    "    #lda = LdaMulticore.load('./models/lda_model_neg')\n",
    "    \n",
    "    return trigram_bow_corpus, lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/gensim-2.3.0-py3.6-macosx-10.7-x86_64.egg/gensim/models/phrases.py:316: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 28min 25s, sys: 1h 34min 54s, total: 3h 3min 19s\n",
      "Wall time: 9h 35min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "bigram_model, trigram_model, trigram_dictionary = reviewPreProcess(neg_scores['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 58s, sys: 21.7 s, total: 4min 20s\n",
      "Wall time: 2h 1min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "trigram_bow_corpus, lda = LDA_Model(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def explore_topic(topic_number, topn=25):\n",
    "    \"\"\"\n",
    "    accept a user-supplied topic number and\n",
    "    print out a formatted list of the top terms\n",
    "    \"\"\"\n",
    "        \n",
    "    print(u'{:20} {}'.format(u'term', u'frequency') + u'\\n')\n",
    "\n",
    "    for term, frequency in lda.show_topic(topic_number, topn=25):\n",
    "        print(u'{:20} {:.3f}'.format(term, round(frequency, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "term                 frequency\n",
      "\n",
      "eat                  0.010\n",
      "like                 0.010\n",
      "$                    0.010\n",
      "taste                0.010\n",
      "chicken              0.009\n",
      "pizza                0.009\n",
      "try                  0.007\n",
      "fry                  0.006\n",
      "sauce                0.006\n",
      "meat                 0.005\n",
      "restaurant           0.005\n",
      "think                0.005\n",
      "sandwich             0.005\n",
      "price                0.005\n",
      "cheese               0.005\n",
      "burger               0.005\n",
      "dish                 0.005\n",
      "flavor               0.005\n",
      "want                 0.004\n",
      "bland                0.004\n",
      "cold                 0.004\n",
      "salad                0.004\n",
      "service              0.004\n",
      "meal                 0.004\n",
      "little               0.004\n"
     ]
    }
   ],
   "source": [
    "explore_topic(topic_number=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "term                 frequency\n",
      "\n",
      "table                0.013\n",
      "restaurant           0.009\n",
      "bar                  0.007\n",
      "like                 0.007\n",
      "drink                0.006\n",
      "want                 0.006\n",
      "people               0.006\n",
      "tell                 0.006\n",
      "ask                  0.006\n",
      "service              0.005\n",
      "eat                  0.005\n",
      "leave                0.005\n",
      "know                 0.005\n",
      "look                 0.005\n",
      "wait                 0.005\n",
      "try                  0.005\n",
      "friend               0.005\n",
      "think                0.005\n",
      "$                    0.005\n",
      "seat                 0.004\n",
      "sit                  0.004\n",
      "menu                 0.004\n",
      "server               0.004\n",
      "walk                 0.004\n",
      "way                  0.004\n"
     ]
    }
   ],
   "source": [
    "explore_topic(topic_number=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "term                 frequency\n",
      "\n",
      "service              0.029\n",
      "wait                 0.017\n",
      "ask                  0.015\n",
      "minute               0.013\n",
      "tell                 0.010\n",
      "customer             0.010\n",
      "manager              0.009\n",
      "server               0.009\n",
      "drink                0.009\n",
      "table                0.008\n",
      "eat                  0.007\n",
      "experience           0.007\n",
      "rude                 0.007\n",
      "horrible             0.007\n",
      "waitress             0.007\n",
      "want                 0.007\n",
      "restaurant           0.006\n",
      "leave                0.006\n",
      "like                 0.006\n",
      "location             0.006\n",
      "people               0.006\n",
      "know                 0.006\n",
      "slow                 0.005\n",
      "staff                0.005\n",
      "terrible             0.005\n"
     ]
    }
   ],
   "source": [
    "explore_topic(topic_number=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assign topic names and save to disk:\n",
    "topic_names = {0: u'food_quality',\n",
    "               1: u'price',\n",
    "               2: u'service_quality'}\n",
    "\n",
    "#with open('./models/topic_names.pkl', 'wb') as f:\n",
    "    #pickle.dump(topic_names, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare for LDA visualization\n",
    "trigram_dictionary = Dictionary.load('./models/trigram_dict_all_neg.dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el757212496411128397041590\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el757212496411128397041590_data = {\"mdsDat\": {\"Freq\": [36.74143167127191, 33.44402079757835, 29.81454753114973], \"cluster\": [1, 1, 1], \"topics\": [1, 2, 3], \"x\": [0.11770727881504844, -0.06935191449860134, -0.04835536431644717], \"y\": [-0.006351907496418254, -0.050237517050706115, 0.05658942454712439]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", 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\"taste\", \"taste\", \"taste\", \"tasteless\", \"tasteless\", \"tasteless\", \"tell\", \"tell\", \"tell\", \"terrible\", \"terrible\", \"terrible\", \"terribly_slow\", \"terribly_slow\", \"terribly_slow\", \"texture\", \"texture\", \"texture\", \"thai_ice_tea\", \"thin_crust\", \"thin_crust\", \"thin_crust\", \"thing\", \"thing\", \"thing\", \"think\", \"think\", \"think\", \"tiene\", \"tiene\", \"toby_keith\", \"toby_keith\", \"toby_keith\", \"tomato\", \"tomato\", \"tomato\", \"topping\", \"topping\", \"topping\", \"total_bitch\", \"tropical_smoothie\", \"tropical_smoothie\", \"tropical_smoothie\", \"try\", \"try\", \"try\", \"tv\", \"tv\", \"tv\", \"und\", \"und\", \"und\", \"une\", \"valet\", \"valet\", \"valet\", \"vegetable\", \"vegetable\", \"vegetable\", \"veggie\", \"veggie\", \"veggie\", \"velvet_rope\", \"wait\", \"wait\", \"wait\", \"waiter\", \"waiter\", \"waiter\", \"waitress\", \"waitress\", \"waitress\", \"walk\", \"walk\", \"walk\", \"want\", \"want\", \"want\", \"way\", \"way\", \"way\", \"wear\", \"wear\", \"wear\", \"wedding\", \"wedding\", \"wedding\", \"west_wing\", \"wie\", \"wie\", \"wie\", \"wine\", \"wine\", \"wine\", \"wir\", \"wir\", \"wir\", \"women\", \"wonton\", \"wonton\", \"wonton\", \"wonton_soup\", \"wonton_soup\", \"wonton_soup\", \"work\", \"work\", \"work\", \"worst\", \"worst\", \"worst\", \"wrong\", \"wrong\", \"wrong\", \"zu\", \"zu\", \"zum\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 3, 2]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el757212496411128397041590\", ldavis_el757212496411128397041590_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el757212496411128397041590\", ldavis_el757212496411128397041590_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el757212496411128397041590\", ldavis_el757212496411128397041590_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda = LdaMulticore.load('./models/lda_model_neg')\n",
    "    \n",
    "LDAvis_prepared = pyLDAvis.gensim.prepare(lda, trigram_bow_corpus, trigram_dictionary)\n",
    "\n",
    "# Save pre-prepared pyLDAvis data to disk:\n",
    "with open('./models/ldavis_prepared', 'wb') as f: \n",
    "    pickle.dump(LDAvis_prepared, f)\n",
    "    \n",
    "# load the pre-prepared pyLDAvis data from disk:\n",
    "with open('./models/ldavis_prepared', 'rb') as f:  \n",
    "    LDAvis_prepared = pickle.load(f)\n",
    "    \n",
    "pyLDAvis.display(LDAvis_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pyLDAvis.save_html(LDAvis_prepared, './models/ldavis_html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_sample_review(review_number):\n",
    "    \"\"\"\n",
    "    retrieve a particular review index\n",
    "    from the reviews file and return it\n",
    "    \"\"\"\n",
    "    \n",
    "    return list(it.islice(line_review(neg_scores['text']),\n",
    "                          review_number, review_number+1))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for description of single review text\n",
    "\n",
    "def lda_description(review_text, min_topic_freq=0):\n",
    "    \"\"\"\n",
    "    accept the original text of a review and (1) parse it with spaCy,\n",
    "    (2) apply text pre-proccessing steps, (3) create a bag-of-words\n",
    "    representation, (4) create an LDA representation, and\n",
    "    (5) print a sorted list of the top topics in the LDA representation\n",
    "    \"\"\"\n",
    "    \n",
    "    # parse the review text with spaCy\n",
    "    parsed_review = nlp(review_text)\n",
    "    \n",
    "    # lemmatize the text and remove punctuation and whitespace\n",
    "    unigram_review = [token.lemma_ for token in parsed_review\n",
    "                      if not punct_space(token)]\n",
    "    \n",
    "    # apply the first-order and secord-order phrase models\n",
    "    bigram_review = bigram_model[unigram_review]\n",
    "    trigram_review = trigram_model[bigram_review]\n",
    "    \n",
    "    # remove any remaining stopwords\n",
    "    trigram_review = [term for term in trigram_review\n",
    "                      if not term in spacy.en.English.Defaults.stop_words]\n",
    "    \n",
    "    # create a bag-of-words representation\n",
    "    review_bow = trigram_dictionary.doc2bow(trigram_review)\n",
    "    \n",
    "    # create an LDA representation\n",
    "    review_lda = lda[review_bow]\n",
    "    \n",
    "    # sort with the most highly related topics first\n",
    "    #review_lda = sorted(review_lda, key=lambda (topic_number, freq): -freq)  # was: key=lambda (topic_number, freq): -freq) ???\n",
    "    \n",
    "    for topic_number, freq in review_lda:\n",
    "        if freq < min_topic_freq:\n",
    "            break\n",
    "            \n",
    "        # print the most highly related topic names and frequencies\n",
    "        print('{:25} {}'.format(topic_names[topic_number], round(freq, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bigram_model = Phrases.load('./models/bigram_model_neg.txt')\n",
    "trigram_model = Phrases.load('./models/trigram_model_neg.txt')\n",
    "trigram_dictionary = Dictionary.load('./models/trigram_dict_all_neg.dict')\n",
    "lda = LdaMulticore.load('./models/lda_model_neg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\gensim\\models\\phrases.py:316: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "food_quality              0.763\n",
      "price                     0.074\n",
      "service_quality           0.164\n"
     ]
    }
   ],
   "source": [
    "# test a review and show the posibility of each problem type\n",
    "lda_description('This gets one star because 0 is not an option and they did have free mediocre cocktails with brunch. Ordered the tuna tartar taco and the kale and eggs salad. Waiter came back later, sorry no tuna tartare. Ok, no problem. Looking forward to the kale with roasted market veg and manchego What arrived was a bowl of kale with some canned garbanzos and eggs. For $16 they could have at least put it on a plate with a side bowl of house potatoes like the other brunch dishes. Asked the waiter where are the roasted market vegetables. He went to check, they took it away, maitre d apologized, said they will bring another. 10 mins later they brought another of the SAME THING. So what changed? I asked where are the roasted  market vegetables - she pointed to the 5 garbanzos You can get a better salad in the fridge at your local CVS.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for updating dataframe with values for each review:\n",
    "\n",
    "def lda_descr_df(review_text, min_topic_freq=0):\n",
    "    \"\"\"\n",
    "    accept the original text of a review and (1) parse it with spaCy,\n",
    "    (2) apply text pre-proccessing steps, (3) create a bag-of-words\n",
    "    representation, (4) create an LDA representation, and\n",
    "    (5) print a sorted list of the top topics in the LDA representation\n",
    "    \"\"\"\n",
    "    \n",
    "    # parse the review text with spaCy\n",
    "    parsed_review = nlp(review_text)\n",
    "    \n",
    "    # lemmatize the text and remove punctuation and whitespace\n",
    "    unigram_review = [token.lemma_ for token in parsed_review\n",
    "                      if not punct_space(token)]\n",
    "    \n",
    "    # apply the first-order and secord-order phrase models\n",
    "    bigram_review = bigram_model[unigram_review]\n",
    "    trigram_review = trigram_model[bigram_review]\n",
    "    \n",
    "    # remove any remaining stopwords\n",
    "    trigram_review = [term for term in trigram_review\n",
    "                      if not term in spacy.en.English.Defaults.stop_words]\n",
    "    \n",
    "    # create a bag-of-words representation\n",
    "    review_bow = trigram_dictionary.doc2bow(trigram_review)\n",
    "    \n",
    "    # create an LDA representation\n",
    "    review_lda = lda[review_bow]\n",
    "\n",
    "    return review_lda  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for returning label of topic with max value:\n",
    "\n",
    "bigram_model = Phrases.load('./data/bigram_model_neg.txt')\n",
    "trigram_model = Phrases.load('./data/trigram_model_neg.txt')\n",
    "\n",
    "def punct_space(token):\n",
    "    \"\"\"\n",
    "    helper function to eliminate tokens\n",
    "    that are pure punctuation or whitespace\n",
    "    \"\"\"\n",
    "    \n",
    "    return token.is_punct or token.is_space or token.like_num or token.is_digit\n",
    "\n",
    "def lda_descr_chat(review_text, min_topic_freq=0):\n",
    "    \"\"\"\n",
    "    accept the original text of a review and (1) parse it with spaCy,\n",
    "    (2) apply text pre-proccessing steps, (3) create a bag-of-words\n",
    "    representation, (4) create an LDA representation, and\n",
    "    (5) print a sorted list of the top topics in the LDA representation\n",
    "    \"\"\"\n",
    "    \n",
    "    # parse the review text with spaCy\n",
    "    parsed_review = nlp(review_text)\n",
    "    \n",
    "    # lemmatize the text and remove punctuation and whitespace\n",
    "    unigram_review = [token.lemma_ for token in parsed_review\n",
    "                      if not punct_space(token)]\n",
    "    \n",
    "    # apply the first-order and secord-order phrase models\n",
    "    bigram_review = bigram_model[unigram_review]\n",
    "    trigram_review = trigram_model[bigram_review]\n",
    "    \n",
    "    # remove any remaining stopwords\n",
    "    trigram_review = [term for term in trigram_review\n",
    "                      if not term in spacy.en.English.Defaults.stop_words]\n",
    "    \n",
    "    # create a bag-of-words representation\n",
    "    review_bow = trigram_dictionary.doc2bow(trigram_review)\n",
    "    \n",
    "    # create an LDA representation\n",
    "    review_lda = lda[review_bow]\n",
    "\n",
    "    return max(review_lda, key=lambda item: item[1])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools as it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def select_review(review_number):\n",
    "    \"\"\"\n",
    "    retrieve a particular review index\n",
    "    from the reviews file and return it\n",
    "    \"\"\"\n",
    "    \n",
    "    return list(it.islice(line_review(neg_scores['text']),\n",
    "                          review_number, review_number+1))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def update_df(df):\n",
    "    \n",
    "    df['food_quality'] = \"\"\n",
    "    df['price'] = \"\"\n",
    "    df['service_quality'] = \"\"\n",
    "    df['problem_type'] = \"\"\n",
    "    \n",
    "    for i in range(0, len(df)):\n",
    "        \n",
    "        result = lda_descr_df(select_review(i))\n",
    "        v1 = [round(x[1], 3) for x in result if x[0]==0]\n",
    "        v2 = [round(x[1], 3) for x in result if x[0]==1]\n",
    "        v3 = [round(x[1], 3) for x in result if x[0]==2]\n",
    "        \n",
    "        df['food_quality'].iloc[i] =  v1[0] if len(v1) > 0 else 0\n",
    "        df['price'].iloc[i] =  v2[0] if len(v2) > 0 else 0\n",
    "        df['service_quality'].iloc[i] =  v3[0] if len(v3) > 0 else 0\n",
    "        df['problem_type'].iloc[i] = (max(result, key=lambda item: item[1])[0])\n",
    "        \n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\gensim\\models\\phrases.py:316: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:179: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1d 8h 1min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_topics = update_df(neg_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assign topic names and save to disk:\n",
    "topic_names = {0: u'food_quality',\n",
    "               1: u'price',\n",
    "               2: u'service_quality'}\n",
    "\n",
    "with open('./models/topic_names.pkl', 'wb') as f:\n",
    "    pickle.dump(topic_names, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_topics.to_csv('df_topics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_topics = df_topics.drop('Unnamed: 0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_topics = df_topics.rename(columns = {'service_quality':'price'})\n",
    "df_topics = df_topics.rename(columns = {'food_quality':'service_quality'})\n",
    "df_topics = df_topics.rename(columns = {'waiting_time':'food_quality'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
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       "  <thead>\n",
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       "      <th>funny</th>\n",
       "      <th>user_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>text</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>useful</th>\n",
       "      <th>cool</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>...</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
       "      <th>filtered_text</th>\n",
       "      <th>sentiment_score</th>\n",
       "      <th>food_quality</th>\n",
       "      <th>price</th>\n",
       "      <th>service_quality</th>\n",
       "      <th>problem_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ujOPJEz_KxzAyZDnji-2Ng</td>\n",
       "      <td>QXWku_OB3FCj9VCZfUZwwg</td>\n",
       "      <td>I wish I could give 1.5 stars. Nothing special...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>['wish', 'could', 'give', '1', '5', 'stars', '...</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>6aEUn50d3Ts7MiGu6WdpKA</td>\n",
       "      <td>5NtaW5EwXK595kP4Ynnisg</td>\n",
       "      <td>Disappointed that on yelp their hours show the...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-09-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-06-27</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>['disappointed', 'yelp', 'hours', 'show', 'ope...</td>\n",
       "      <td>-0.040000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.621</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>alTlRb9qMBX11pARX05Big</td>\n",
       "      <td>jJaU6pbKkYgl15P-5zfg3A</td>\n",
       "      <td>Everything was good except Thai chicken wraps....</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-05-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010-09-17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>['everything', 'good', 'except', 'thai', 'chic...</td>\n",
       "      <td>-0.040000</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>R-kL1bocHgP4GW7Mgd-ZXA</td>\n",
       "      <td>nI2rhDM2CgNazMdhiODoRQ</td>\n",
       "      <td>OH MY GOD, this place. I used to like Pei Wei ...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-12-04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>['oh', 'god', 'place', 'used', 'like', 'pei', ...</td>\n",
       "      <td>-0.022989</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>mCE5jUDzP-ihU_6cwQsiiw</td>\n",
       "      <td>FxAxYxj5SbiOtcK36bJxCA</td>\n",
       "      <td>We went as a family of 6 and was treated very ...</td>\n",
       "      <td>jQsNFOzDpxPmOurSWCg1vQ</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-02-26</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>['went', 'family', '6', 'treated', 'poorly', '...</td>\n",
       "      <td>-0.057692</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   funny                 user_id               review_id  \\\n",
       "0      0  ujOPJEz_KxzAyZDnji-2Ng  QXWku_OB3FCj9VCZfUZwwg   \n",
       "1      0  6aEUn50d3Ts7MiGu6WdpKA  5NtaW5EwXK595kP4Ynnisg   \n",
       "2      1  alTlRb9qMBX11pARX05Big  jJaU6pbKkYgl15P-5zfg3A   \n",
       "3      0  R-kL1bocHgP4GW7Mgd-ZXA  nI2rhDM2CgNazMdhiODoRQ   \n",
       "4      0  mCE5jUDzP-ihU_6cwQsiiw  FxAxYxj5SbiOtcK36bJxCA   \n",
       "\n",
       "                                                text             business_id  \\\n",
       "0  I wish I could give 1.5 stars. Nothing special...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "1  Disappointed that on yelp their hours show the...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "2  Everything was good except Thai chicken wraps....  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "3  OH MY GOD, this place. I used to like Pei Wei ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "4  We went as a family of 6 and was treated very ...  jQsNFOzDpxPmOurSWCg1vQ   \n",
       "\n",
       "   stars        date  useful  cool yelping_since      ...       is_open  \\\n",
       "0      1  2017-07-08     0.0   0.0    2017-07-01      ...           1.0   \n",
       "1      2  2015-09-22     1.0   0.0    2013-06-27      ...           1.0   \n",
       "2      3  2014-05-29     0.0   1.0    2010-09-17      ...           1.0   \n",
       "3      2  2016-02-14     0.0   0.0    2010-12-04      ...           1.0   \n",
       "4      1  2011-02-27     0.0   0.0    2011-02-26      ...           1.0   \n",
       "\n",
       "                                          categories  stars_dif bus_stars_dif  \\\n",
       "0  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -2.67          -2.5   \n",
       "1  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -2.13          -1.5   \n",
       "2  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -0.48          -0.5   \n",
       "3  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -1.63          -1.5   \n",
       "4  ['Fast Food', 'Gluten-Free', 'Asian Fusion', '...      -1.21          -2.5   \n",
       "\n",
       "                                       filtered_text sentiment_score  \\\n",
       "0  ['wish', 'could', 'give', '1', '5', 'stars', '...       -0.033333   \n",
       "1  ['disappointed', 'yelp', 'hours', 'show', 'ope...       -0.040000   \n",
       "2  ['everything', 'good', 'except', 'thai', 'chic...       -0.040000   \n",
       "3  ['oh', 'god', 'place', 'used', 'like', 'pei', ...       -0.022989   \n",
       "4  ['went', 'family', '6', 'treated', 'poorly', '...       -0.057692   \n",
       "\n",
       "  food_quality  price  service_quality  problem_type  \n",
       "0        0.968  0.016            0.016             0  \n",
       "1         0.02  0.359            0.621             2  \n",
       "2        0.964  0.018            0.017             0  \n",
       "3        0.384  0.349            0.267             0  \n",
       "4        0.271   0.41            0.319             1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_quality = df_topics.loc[df_topics.problem_type==0,:]\n",
    "price = df_topics.loc[df_topics.problem_type==1,:]\n",
    "service_quality = df_topics.loc[df_topics.problem_type==2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26e9036cc18>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OnUq9W9nfvnQKbbO5ug/deR1eev28Zu5wcQ5wqawW4PiQcvrHkZNj+MKdG7DK38Tcx+C5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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e8c740080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(food_quality['sentiment_score'], food_quality['food_quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26e83f802b0>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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HxWbU+5VeD69HvW3y/7V5BdpbXZgOceDjAv7i+4cLGr/FbMKi1gbZdtakPrz7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\niAN9I7oe53XZEIwmkBCoy0a1SiSBfb1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cU184p0YE8MT/fBeTQT5VFq6lcbbU3W/dspQC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\nTERERESjzv8Half6an6B3wQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e8639aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(price['sentiment_score'], price['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26e8ec382e8>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Oq/QJlkom3am9xvLnKdcVcOEnCrvE88MtlgZaMMxoX7Y00IKEmNG9Ti1hjU12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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e90c8b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(service_quality['sentiment_score'], service_quality['service_quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distplot2 = df_topics.loc[:,['sentiment_score', 'stars_dif', 'food_quality', 'price', 'service_quality', 'problem_type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment_score</th>\n",
       "      <th>stars_dif</th>\n",
       "      <th>food_quality</th>\n",
       "      <th>price</th>\n",
       "      <th>service_quality</th>\n",
       "      <th>problem_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.033333</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.040000</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.621</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.040000</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.022989</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.057692</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment_score  stars_dif food_quality  price service_quality problem_type\n",
       "0        -0.033333      -2.67        0.968  0.016           0.016            0\n",
       "1        -0.040000      -2.13         0.02  0.359           0.621            2\n",
       "2        -0.040000      -0.48        0.964  0.018           0.017            0\n",
       "3        -0.022989      -1.63        0.384  0.349           0.267            0\n",
       "4        -0.057692      -1.21        0.271   0.41           0.319            1"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distplot2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:475: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  grid,delta = np.linspace(a,b,gridsize,retstep=True)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:475: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  grid,delta = np.linspace(a,b,gridsize,retstep=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x26e8e08f2b0>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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T0E/+vlbvfV6k9z4v0k/+vlYel6kLa9fO/eYxA1RcFdEt/1inj78o0b/W7dNPX1mnvlle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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e8e08f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "#ax.set_aspect(\"equal\")\n",
    "\n",
    "# Draw the two density plots\n",
    "ax = sns.distplot(distplot2['food_quality'], color=\"r\")\n",
    "ax = sns.distplot(distplot2['price'], color=\"b\")\n",
    "\n",
    "\n",
    "# Add labels to the plot\n",
    "red = sns.color_palette(\"Reds\")[-2]\n",
    "blue = sns.color_palette(\"Blues\")[-2]\n",
    "\n",
    "ax.text(0.05, 8.0, \"Food Quality\", size=16, color=blue)\n",
    "ax.text(0.15, 1.5, \"Price\", size=16, color=red)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:475: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  grid,delta = np.linspace(a,b,gridsize,retstep=True)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:475: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  grid,delta = np.linspace(a,b,gridsize,retstep=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x26e8c11ce48>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DDXywMpPVe/MY37dF5VzasCAzOUXllcdwud1sPlBY+fX2rCLsTjcT+rXEdmxEucTuZNP+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\nQgghhBBhQRJfIYQQQggRFv4/95EFPRDzDnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e9c996ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "#ax.set_aspect(\"equal\")\n",
    "\n",
    "# Draw the two density plots\n",
    "\n",
    "ax = sns.distplot(distplot2['price'], color=\"b\")\n",
    "ax = sns.distplot(distplot2['service_quality'], color=\"g\")\n",
    "\n",
    "# Add labels to the plot\n",
    "\n",
    "blue = sns.color_palette(\"Blues\")[-2]\n",
    "green = sns.color_palette(\"Greens\")[-2]\n",
    "ax.text(0.05, 8.0, \"Food Quality\", size=16, color=blue)\n",
    "\n",
    "ax.text(0.9, 5, \"Service Quality\", size=16, color=green)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:475: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  grid,delta = np.linspace(a,b,gridsize,retstep=True)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:475: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  grid,delta = np.linspace(a,b,gridsize,retstep=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x26e970d0320>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Huab5xczes5jxG74947E3XHAl3ep2oG5ELf438GlaVm/CgIYXMX/vMrw+X1m72XsWEx0S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\nnOZJ6u0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e98350f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "#ax.set_aspect(\"equal\")\n",
    "\n",
    "# Draw the two density plots\n",
    "ax = sns.distplot(distplot2['food_quality'], color=\"r\")\n",
    "\n",
    "ax = sns.distplot(distplot2['service_quality'], color=\"g\")\n",
    "\n",
    "# Add labels to the plot\n",
    "red = sns.color_palette(\"Reds\")[-2]\n",
    "\n",
    "green = sns.color_palette(\"Greens\")[-2]\n",
    "\n",
    "ax.text(0.15, 1.5, \"Price\", size=16, color=red)\n",
    "ax.text(0.9, 5, \"Service Quality\", size=16, color=green)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f72b9bba8>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fbW1tfY4HAoE+x/s7NykpacBBm5ubP9TCRpqmpqZYjzBisFeDE819+qAY9fsSprm5WT/4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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f72b9beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,6))\n",
    "bar_df2 = distplot2.groupby('problem_type')['problem_type'].count().sort_values(ascending=False)\n",
    "bar_df2.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:342: DeprecationWarning: pandas.core.common.is_categorical_dtype is deprecated. import from the public API: pandas.api.types.is_categorical_dtype instead\n",
      "  elif is_categorical(y):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x26ec33dacc0>,\n",
       " <matplotlib.text.Text at 0x26e8f74b9e8>]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qb7VjmmqGnILjn98w8Exne5/1n8krTMkIJUIInNQi2BLw43RsqLsZM2bhhhtu\nRnV1La/AXgAGaoo7c6YNr776IjZseAeGYUC25MFcPA9KTmpmL0p3oNZD7miQdkcvfU+dOg0rV65G\nVVU1v1QoJYQQ2Lt3N/72t79i585tAKJ9ra1Tc2GZnAPZktwWK9dfT0AIgcLrJiV1OwCge8IIHHYj\nfMILoRlQVRXLli3HFVdcg4qKCUnfPlFPJ0824i9/eQWbNn0AwzBQIJuw0BYN1sn+/n+20xWfPRAA\n8mUT1uQVJHWbQgg0aRF81CNI19QswPXX34SZM2cndduZioGa+mhtbcErr/wJH3zwHoQQkK0FsJTO\nh+JI7s0/hhaAr+GlPusdM25J6gyQRsSPUNsuaJ3HAAhUTpqMVStXo6aGZ+eUPqdPN+Ott17Hexve\nQTgUgmSSYJmaC9vM/KQFa9dfoyeTBddWJuXzAUBzhxHY247wKX90W4WFuOLyq7F8+eVwOtN/8zFl\nt9bWFrz66kv44IN3o8HaZMIiqx1Tkxisz+oaXnB3wEA0TF/tzEGRKTlXQ+NBOujH6di4+QsW1OPm\nm2/DpEmTk7LNbMFATQNqbj6Fl176H2zevBFA9OZFS+l8mKz5Sdum9/CfIcLd/3ayOQeOaTckZVtC\njyB8dh8irgMQho7x4yfg1ltXY8GChQzSNGr4/X5s2PC/eP2N1+Bqb4ekyrDOyINteh4kJbGXhJMZ\nqHW/hsC+doROeAERvQJ0zTU3oK5uEUyjZHgwoi5dDUsbN26AYRgoNJlwic2BiiT1Jf5dZzuEELg7\nvygpnw9Eg/sGvw+nYn2ka2vrcMstt2HSpClJ22Y2YaCmIR07dgR//OOz2L9/LwAJat5kmEuqIauJ\nH7hdD3bAf/R1AAKyOQfWimUJD/BC6Ii4DiN8Zg+EHkJefgFuXbUay5Yt501PNGpFImGsX/8WXn31\nT/B6vZAtJlhn58M6JTdhfayTEaiNkI7AgQ4Ej7gBQ6CiYgJuu+0OzJ9fxxNXGvVaWk7Hg7UQAtNV\nCy62O+BI8LHid7F+1J/OK0zo5wLRGQ0/CvixKxSEAFBdPR8rV34SU6ZMS/i2shkDNZ0XIQR27dqO\nP/7xOZw6dRKSbIJaOAfmotmQ5MRemvIeehlCCOTMuCWhnwsAmvcUQi3bYIQ9sFituOH6m3HVVdfB\nYrEkfFtEyRAI+PH666/h9df/jFAoBJNDhaO+BGrxyG/eS2SgFkIgdMwD/652CM1AUVExVq1ajaVL\nl/HElcbQKbKaAAAgAElEQVSco0eP4He//W8cPXYEqiRhkdWOaos1YTcuJiNQCyHQEAlhY8APv2Gg\npKQUd931/6GmpjZh26BuDNR0QQzDwPvvv4v/+Z/n4XZ3QlIdsJTWJvTGRe+hlwEAzuk3J+TzAMAI\nexBs2QbdewqyLGPFiitwyy23IjeXw9/R2OR2u/Hqq3/CW2+9AQEB28x82OYUjKi1OlGB2gjp8H7c\nhkizHza7HatWrsaKFZdDVUfHrItEw2EYBt59dz3+54U/wOf3odik4EqHEwUJ6O+c6EDtNwy87fOg\nUYtAVVTccOMtuO66Gzn8XRIxUNOwBAIBvPLKn/C3v/0Fuq7D5CiDpawOJsvIA2oiA7UwIgif2Ytw\n+wFAGJg9ey4+9am/w4QJybvpiiiVDh7cj//8zydx9uwZmPItyFlUAlPO8A6aiQjU4dN++La2wQjp\nmD17Lu6990soLExev1CiVPN43PjjH5/F+++/C1WScKnNgVkjHN4xkYG6MRLGW34vAoaBefNqcPfd\nn0VJSemIP5cGx0BNI9LcfArPPfdb7N69A4AEy7h6mAumj+gzExWojZAbgcb/hRHxo7CwCHfe+WnU\n1y9mv03KOH6/H88++ww++OA9SCYZjoXFsFRc+IgZIwnUQgj497QjeLATJkXBbbfegauvvo7dOyhj\nbd68Eb/+9X8iGAxiptmCS+0OmIc5KVoiArUuBD4K+rEtGIDJZMLq1Wtw5ZXXch9MEQZqGjEhBHbs\n2Ib//u+n4PV6YCmrh7lwxrA/LxGBWg91InBiPYQWxPXX34ybblrFftKU8TZv/hBPP/0UQqEQ7NWF\nsE7Pu6ATyOEGaqEb8G5pQ7jJh7KycfjSl/4BlZXJH8uaKN1aW1vw1FOP4+jRwyg0mXCjMxcO+cJH\nrRlpoI4Igb963TipRVBSUor77vsqpkyZOqzPouEZKFCbHnnkkUdSW0pi+f3hdJeQNSRJwrhx5aip\nWYAtWzbB334MkskMk214l3nD7dGp0M2Fs4b1+z3D9Kc+9RnceONKznBIWaGiYgJqamqxbftWeE+4\nYIQNqGW28w7VwUOdAADb9PPvumWEdHjeP41IawAzZ87G/fd/i5eXKWs4HE4sW7YcXq8H+44cxpFI\nGJNUM6wX2Cq8MxQAANRYL3zOhYBh4FWvG826hvnzF+DrX3+I+2AaOBz9N9rx+gBdsIqKCXjwwe8g\nNzcPoZaPETqzD6m+0KEH2hE4/jaEFsSnP30PrrzympRunyjdKisn4zvf/hdUVExE6Igb3k0tEEZy\n9kPdr8H9zilo7SEsWXIxvv71hzg5C2UdRVHw6U/fg5UrPwmPYeBP3k60xSZNSTavoeNFbydadQ3L\nli3H2rVfg82W+CFtafgYqGlYxo+vwIMPfgcFBYUIt+1AqOVjCGGkZNua9xQCJ94GjDA+85l7cfnl\nV6Vku0SjTWFhER566GHMnj0X4VN+eJIQqnW/Bs97zdC9EVx77Y34/Oe/zFE8KGtJkoSbb74Vd999\nD4JC4BWvG2f15IZqn2HgJY8bHbqOa6+9AZ/97Bc5SdIoxEBNw1ZePh7/9E//FxUVExBxNSDYtBHC\n0JO6zUjHEQQa34PJJOHv//7/YMWKy5O6PaLRzm634x/+4QHMnTsPkebEhup4mPZFcOONK7F69Rre\n+EQE4LLLrsJnP/tFhISBV7xudOrJOfYFDQOveDvhNnTceONK3H77XbzhfpTiNyONSFcL2axZc6B5\nGhFs+iBp24p0HkeweTPsdjseuP9bqKtblLRtEY0lFosFX/3q/fFQ7dt2ZsSfKQwBz4ZomL7pplVY\ntWo1D+REPSxbthx33fUZBGKh15vgBqWIEPiz1w2XruPKK6/BqlWrE/r5lFgM1DRidrsDX/vag9FQ\n7W2C5m1O+DaEoSHUuh2KquKhhx7GjBnDu5GRKFOZzWZ89av3Y1z5eIQavTDCIzu4R1r80L0RLFu2\nHCtXfpJhmqgfV1wRDboew8AHfl9CP3tb0I9WXcPFF1+KO++8m/vgKMdATQmhqmasWRPd4UOtOxJ+\nk2K4vQFCC+Dqq65DRcWEhH42UaYwm824ZNlywBAIN43s4B5q9AIALrvsSh7IiQZx440rMWXyVByO\nhNGiRRLymV5Dx45QEHl5+bj77nvY1WoM4L8QJUxl5WQsXboMRqgDkY4jCftcI+JHpH0v7A4Hrr/+\npoR9LlEmWrp0GQAgeMwz7BNbI6gh0uxHadk4TJkyLZHlEWUcSZJw+x13AQA2BnwJaVD6KOCHJgRW\nrVoNywhnZ6TUYKCmhLr11tths9kQbt0GPdQ54s8Twoje7KhH8Mnb7oTd7khAlUSZq7CwCPX1i6G7\nQvHxpi+EEALej89A6AJXXXkNW6eJzsOsWXNQW1uHZk1D4whbqTt0HQfCIYwvr8All6xIUIWUbAzU\nlFBFRcW4554vQBgagk0fQBgjG04ofGYP9EAb6usXc0QPovN09933wJmTA/8eF7TOC5v8KnTMg8hp\nP2bPrsJll3FISqLztXJl9KbBjwL+EbVSbwn6IQCsXLWaXT3GEP5LUcItXLgEl112JYxQJ8Jndg/7\nc/RAO8Jn9sRC+ufZUkZ0nnJz8/DZe74AGAK+j9v6vG6ucMBc0fdqjxHU4N/VDpvdjnvvvY8Hc6IL\nUFk5CQsXLkarruHEMFupO3QdDeEQJk6sRF3dwgRXSMnEb0tKijvv/DTsdgci7hPDPlPXPCcAAGvW\n3M2uHkQXqLa2HtXV86G5QtD9va8UOaqL4Kgu6vM74ZYAhGbgxhtuQWFh39eJaHA33HALAGB/KDis\n3z8Qjv7eddfdzBPaMYb/WpQUqmrGvHnVEBE/jLB7WJ+heZuhKAqqqmoSXB1Rdpg3L7rvRNoC5/V+\nLfa+efPmJ60mokxWWTkZ5eXjcTwSQfgCZw8WQqAhHILFYsGCBfVJqpCShYGakqa6uhYAoHlOXvDv\nGmEvjFAnZs+ugsViSXRpRFlhzpwqANExpYciDIFIawBOZw6HpiQaJkmSsHTpMugQOBS+sPsXmjUN\nHsNAff1iHvfGIAZqSpra2jpYLFZEXA0XfHNi+Ox+AMDSpRcnozSirDB+/ASMGzce4SYfdO/gfTpD\njV4YQR0LFy7mpWaiEbjkkhVQTAq2BwMwLqDL48fB6InvJz5xRbJKoyTityYljcPhxJVXXgOhBRFx\nHT7v3zMiPkQ6j6C0dByWLGGgJhouWZaxcuVtgAAC+10Dvk8YAoH9LpgUBTfeuDKFFRJlnoKCQlxy\n6Qp0GjoORUK9XpuqWjBV7dv63KJF0KhFMHv2XEyfPjNVpVICMVBTUl1zzfWwWKwIt++DOM/+ZOGz\n+wFh4OabV8FkMiW5QqLMtnDhElRUTECo0Qvd138rdfikF4ZPwydWXM6bEYkS4LrrboIsy9gV7H1z\n4sV2By7u5yb73bGbGHlCO3YxUFNSOZ05uOiiZRBaEHrg7JDvF0JA8zbB4XCwdZooAWRZjl5CFoB2\npv+RByKt0ZsRP/GJK1NZGlHGKikpxfTpM9GqawgagzcmCSHQGIkgLy8/ft8DjT0M1JR01dXREQN0\nb/OQ7zXCboiIH1VV1WydJkqQyZOnAgC0jlC/r2sdYZgtFpSXj09lWUQZbe7ceQCApiHGpD6r6wgI\nA3PnzuN8C2MYAzUl3ezZVZBlEzTf6V7rlZxKKDmVvdbpsfdw2C6ixJk4cRJkWYbW0XfUAaEb0D1h\nVMbeQ0SJ0TXk64nI4KN9nNDCsfdXJ70mSh4llRsLBoN44IEHcPbsWTgcDjz66KMoLCzs9Z5f//rX\n+POf/wwAWLFiBdauXZvKEikJbDYbJkyYgBMnT0IIET8Dt5bV9nmvHuwAAEydOj2lNRJlMrPZDIfD\nCX+473jUImIAAn2+i4loZKZMmYq83Dwc83hgCAF5gNbno+EwZFlGTc2CFFdIiZTS5ojnnnsOM2fO\nxLPPPouVK1fiySef7PV6Y2MjXn75ZfzhD3/AH//4R2zYsAH79+9PZYmUJKWl4wBDh9AGn2BChD2Q\nJAmlpWUpqowoOyiKAqH3HcJLGNF1JlNK21eIMp4sy1hQtxBBYeC01v/QsV5DR6uuYdasOXA6nSmu\nkBIppYF669atuPTSSwEAy5cvx8aNG3u9Pm7cOPzqV7+CyWSCJEnQNI2Dm2eIcePKAQBGqHPA9wgh\nYIQ9KC4ugaLw4E6USKqqAkY/Y+LGQjb3OaLE65rgrHmAftQtsaDdda8RjV1J+wZdt24dnnnmmV7r\nioqKkJOTAwBwOBzweDy9XldVFYWFhRBC4Ec/+hHmzp2LKVOmDLqdggI7FIU3r412S5cuxKuvvoiI\n+zgUZ3m/79F9zRB6CLW181FSkpPiCokyW2XlRLRuaYHui8DkUOPrI2ejNypOnz6F+x1Rgs2ZMw0A\n4Db0fl/vWj9r1jTuf2Nc0gL16tWrsXr16l7r1q5dC5/PBwDw+XzIzc3t83uhUAjf+ta34HA48PDD\nDw+5HZdr6Cl1Kf3Ky6egrGwcWlpPQJQugKT0vfIQdh0CAFx00Qq0tXn6vE5Ewzd37nxs2bIF4WY/\nbNPz4uvDzdHv5Bkz5nG/I0owk8kOSZIGDNSdenS92ZzD/W+MGOjEJ6VdPurq6vDOO+8AAN59913U\n19f3el0IgS9/+cuYNWsWvvvd73LYtAwiSRIuu+xKQBiIuI/1ed2IBKB7mzFlytT4EF9ElDjz50dv\neIqc7m6EEIaA1hrAuHHl8W5ZRJQ4qmpGeXkFWnQd4XMmNxNC4KQWgcViQWlpaZoqpERJaaBes2YN\nGhoasGbNGjz//PPxETyefvppvPXWW3jzzTexefNmvPfee7j77rtx9913Y9u2bakskZKotjZ6AqUH\n2vu8ZgTbAQjU1i5McVVE2aGgoBCFhUXQPd19OY2ABqELjqpDlESLFy+FLgSOhXsPn9eqa/AYBhYs\nWAhVNaepOkqUlN6FYrPZ8Nhjj/VZf88998SXd+3alcqSKIWKi0ugqir0kLvPa0Y4um78+IpUl0WU\nNQoKCtDuOhsfvtIIaPH1RJQcixYtxYsvvoAjkTBmWqzx9UdjAXvx4qXpKo0SiKP4U8rIsoxx48qj\nsyGK3qMNdIXs8eM5UxtRsuTnFwICEKFov00joHevJ6Kk6BoGNnzOcS8Ue15aOi7lNVHiMVBTShUV\nlQBCB4zeQwh1jU9dWFicjrKIskJRUXT/0v3RlmnDH+m1nogSLxxriVbOmdel63k4HEpxRZQMDNSU\nUl0juwgt2Gu90IKwWKwcd5woicrKoi1lhjcapHWv1ms9ESVeJBLd3yT0TtRy7HnX6zS2MVBTSuXm\nRofrMnrMmCiEgNAC8THKiSg5SkqiwVnvCtS+CCRJQnFxSTrLIspoTqcTdrsD7Xrv2RK7npeVsctH\nJmCgppSaMiU6yL3mPRVfZwTbIfQQRxogSrKJEycBADRXCMIQ0DvCGDeunCMMECWRLMuYOnUa3IaB\ngBEdOk8IgRZdQ3FxSbyhicY2BmpKqerq+XA4nNDcxyFiY3JGOo8BAC666JI0VkaU+fLy8jBuXDm0\ns6FoqNYMzJo1J91lEWW8rgajtlirtMcwEBICU6dOS2dZlEAM1JRSiqJg8eKLILQgdH8bAEDzNMLp\nzEFVVXWaqyPKfDNnzobQDISORkfWmTFjVporIsp8FRUTAACu2MyI7bHHiorKtNVEicVATSmnqrHh\nz6WuPz8ZsixzZkyiFPD5olONy7bofuj3+wd7OxElQHl5dI4FV6yF2mVEHzn3QuZgoKaUO3LkMAAJ\nJmt0MgmTrRBudydcrr4zKBJR4gghcPDgPsg2BZZJ0ZuADx7cn+aqiDJf1033wdjY0yEj+uh0OtNW\nEyUWAzWllBACx48fg2zJhSRHW8jkWLA+duxIOksjynitrafh8XigFFkgOxTIFhMaGg6kuyyijNfc\nHL0RPz92JTYv9nj6dHPaaqLEYqCmlJIkCfn5+RARP4QRm60t7AEA5Odz+mOiZLLZHAAAI6gDhoDQ\nDDgcjjRXRZT5mppOAgAK5WiQLowF6q71NPYxUFPKLViwEMKIQPe3QAgDuvcU8vMLMHny1HSXRpTR\ncnNzMWnSFGjtIYRP+yF0gerq+ekuiyjjqaoKIDq6R8/HrvU09jFQU8rV1S0EAGieJuj+MxB6GHV1\nCyHL/HMkSrZ582oAQyCw1wUAqKqqSXNFRJlv0aIlsFgs2BsOwhACe0LRyc0uuWRFmiujRGGCoZSb\nMCE6TJChBSBiMyZ2rSOi5Cotjc2W6InOlshZ2oiSz2az4+KLL4XXMLA7FMQpTcPcufNQXj4+3aVR\ngjBQU8qZzbFZ2QwdQkSHDuJlL6LUOHd4Sl4ZIkqNroYjd+z+ITYkZRZ+k1LKmUwmyLIcDdOxL5Z4\nyCaipJJlBmqidOjs7AAAlJiiI1x1dLjSWQ4lGL9JKeUkSYoGaEOPj/RhsVjSXBVRdpBl6ZznPAwQ\npUJXoC6KBWq3uzOd5VCC8ZuU0sJisUIYGhDr8mGxWNNcERERUfI4HNFJXDpjDUkcsjKzMFBTWlgs\nFkBo0VANwGxmCzUREWWukpJSAEBjJNzrOWUGBmpKC4vFGu0/bXS1UDNQE6XCuTcA84ZgotToCtCn\ntOgIO8XFDNSZhIGa0sJisUAY3S3UDNREqWG12no9Z3crotTIyckFAHTGJnXJzc1NZzmUYAzUlBbR\nUT0EhBHp8ZyIks1q7Q7QZouFNyUSpYjT6ez1vKtPNWUGfpNSWsQvM+uR2HMGaqJU6Bmoey4TUXKd\nu7+de7WIxjYGakoLU2zYoK6JXRRFSWc5RFmjZxcPK7t7EKVMJKL1eq7F+lJTZmCgprRQlOjkEl19\nqM+dvY2IkqPn/QocXYcodcLh0DnPw2mqhJKBgZrSoqvvmAh7YLc7IEnSEL9BRInQs4XaZuMlZ6JU\nOfd+BR73MgsDNaVFfn5hfLmgoHCQdxJRIvXsXmWz2dNYCVF2yc3N6/U8Ly8/TZVQMjBQU1oUFBT0\nu0xEqcMWaqLUURQFOTk58ec89mUWBmpKi8LCon6XiSh17Ha2UBOlUtfVWVVVeYUowzBQU1oUFRX3\nu0xEqcMDOlFqFRVFG5BsNhv7UGcYBmpKi8LC7n7T+fm87EWUDhxdhyi14jfkizQXQgnHQE1p0d5+\nNr7scrWnsRKi7KVp2tBvIqKE6ezsAMAxqDMRAzWlxZEjh+PLR48eHuSdRJQsHR2udJdAlDWEEPFj\nXyAQgNvtTnNFlEgM1JQWx48fjS8fPXokjZUQZRfDMOLLvDpElDouVzv8fl/8+cmTJ9JYDSUaAzWl\nhaqa48tms3mQdxJRIvW8EUpR1DRWQpRdes5SCgBWq3WAd9JYxEBNaVFaWhZfLisbl8ZKiLJLz0Cd\nl5c3yDuJKJHsdgdUtfsklpOaZRYGakqLniG6tJSBmigdcnJy010CUdaQJCkeoiVJ6jNzIo1tDNSU\nFsXFJT2WOQ41UTqwuxVRanVdnXU4nBy2MsMwUFNa9DwzLyjgTIlERJT58vLyAYCTumQgBmpKC0VR\n4ssFBZzYhYiIMp8sR2MXA3XmYaAmIspSPKgTpVbXPsd9L/MwUFPaBQKBdJdARERENGwM1JQWPSeX\nCAaDaayEiIiIaGTOK1D7/X7s378fQgj4/f5k10RZoGeg1nUtjZUQERERjcyQgXrjxo245ZZb8OUv\nfxltbW24/PLLsWHDhlTURkREREQ06g0ZqH/605/i2WefRW5uLkpLS/G73/0OP/rRj1JRG2UJIUS6\nSyDKStz3iIgSY8hAbRgGSkq6J+GYPn16Ugui7NBzQHtOLkGUHhxpgIgoMZSh3jBu3DisX78ekiTB\n7Xbj97//PcaPH5+K2iiD9TyQm82WNFZCRERENDJDtlB/97vfxSuvvILm5mZcddVV2LdvH7773e+m\nojbKEhYLAzVROrDLB1FqcZ/LXEO2UP/mN7/BT3/601TUQlmqZ/cPIkoddvkgIkqMIVuo169fn7Az\nqmAwiK985Sv41Kc+hc9//vNob2/v932GYeDee+/Fc889l5Dt0ujGgzoREWUDHu8y15At1Pn5+bj2\n2mtRVVXV69L8D37wgwve2HPPPYeZM2fiK1/5Cv785z/jySefxLe//e0+7/v5z38Ot9t9wZ9PRETn\nj5efiYgSY8hAvWrVqoRtbOvWrbj33nsBAMuXL8eTTz7Z5z1//etfIUkSLr300oRtl0a3npO8EFHq\nsLWMiCgxzitQHzx4EJs3b4amaViyZAnmzJkz5AevW7cOzzzzTK91RUVFyMnJAQA4HA54PJ5erx88\neBCvvvoqHnvsMTzxxBPn9R9QUGCHorAP7lhmtysoKclJdxlEWcfhsHDfI0ohmy06TKwsS9z3MsyQ\ngfrFF1/E448/jiuvvBKGYWDt2rX40pe+hE9+8pOD/t7q1auxevXqXuvWrl0Ln88HAPD5fMjNze2z\nrZaWFnzmM59BU1MTVFVFRUUFli9fPuB2XC5OhT7WnTnTibY2z9BvJKKE8nqD3PeIUigQCAMADENw\n3xujBjoRGjJQP/3001i3bh0KCgoAAPfddx/+7u/+bshA3Z+6ujq88847qKmpwbvvvov6+vper3/j\nG9+IL//Hf/wHiouLBw3TlBlMpiH/DImIiIhGrfOaKbErTANAYWHhsPvdrVmzBg0NDVizZg2ef/55\nrF27FkA0tL/11lvD+kwa+6xWa7pLICIiIhq2IZsGZ82ahX/913+Nt0ivW7cOs2fPHtbGbDYbHnvs\nsT7r77nnnj7rvvKVrwxrGzT2cGIXIiIiGsuGbKH+3ve+B7PZjG9961t46KGHYDab8fDDD6eiNiIi\nSiKO8kFElBhDtlCrqoq6ujo88MADaG9vx9tvvw2Hw5GK2ihLaJqW7hKIiIhShiezmWfIFupvf/vb\neOONN+LPN23axBZqSigGaqL04MQuRESJMWQL9e7du/HKK68AiN6Q+OMf/xg33XRT0gsjIqLkYisZ\nUXrwZDbznNcoH62trfHnZ8+ehSwP+WtE540HdSIiIhrLhmyhvu+++7Bq1SrU19dDCIGdO3fin/7p\nn1JRG2UJnqARpQdbyYiIEmPIQH3TTTdh8eLF2L59OxRFwXe+8x2UlpamojbKEpLEQE2UDrw6RESU\nGIMmmQMHDqCtrQ1lZWUoLy/HBx98gPfeey9VtVGWYAs1ERERjWUDJpkXX3wRX/rSl3D69Gk0Njbi\nM5/5DJxOJ95991088cQTqayRMpwss5WMKB3Y5YMoPXh1KPMMGKifeeYZvPDCC6iursZLL72EJUuW\n4B//8R/xb//2b3jttddSWSNlOJPJlO4SiLISD+pE6cGT2cwzYKA2DAOFhYUAomNPr1ixAgCgKEN2\nuya6ILLMQE1ERNmDJ7OZZ8BALUkSwuEwOjo6sG3bNlxyySUAAJfLBV3XU1YgZT72oSZKD7aSEREl\nxoDNzatXr8Ydd9wBAFixYgUmTpyIjRs34mc/+xluv/32lBVImY+Bmig92EpGlB48mc08Awbqu+66\nC9XV1Whra8Py5csBAC0tLbjzzjtx6623pqxAynwM1ERERDSWDdohuqamptfzlStXJrUYyk4M1ETp\nwVYyIqLEYJKhtOPELkTpwS4fRESJwSRDacdjOhEREY1lQwbqp556qs+6n/70p0kphrITrzoTpQe7\nfBARJcaAfah/8pOf4OzZs3j77bdx7Nix+HpN07Bz50587WtfS0V9lAXYQk2UHuzyQZRa3Ocy14CB\n+uqrr8bhw4fx4YcfYvHixfH1JpMJf//3f5+S4oiIiIiIRrsBA3VNTQ1qampw5ZVXIicnJ5U1ERFR\nCrDLB1FqcZ/LXEPOI/7mm2/ihz/8IdxuN4DoH4MkSdi3b1/Si6PswC8YovTg5WciosQYMlA//vjj\n+O1vf4uZM2emoh7KQqFQKN0lEBEREQ3bkKN8lJWVMUxTUoVCwXSXQJSVeHWIiCgxhmyhrqqqwle/\n+lUsW7YMFoslvp6zJhIRjW3s8kFElBhDBmqv1wuHw4Ht27f3Ws9ATYnCmRKJiIhoLBsyUP/gBz8A\nAHR2diIvLy/pBVH2kWUGaqJ0YJcPIqLEGDLJ7N+/H9deey1uueUWtLS04KqrrsKePXtSURtlCVVV\n010CUVZilw8iosQYMlD/y7/8C5544gnk5+ejrKwMjzzyCB5++OFU1EZZwmw2p7sEIiIiomEbMlAH\nAgFMmzYt/nzZsmUIh8NJLYqyDVvJiNKBXT6IiBJjyECdn5+P/fv3xy8Nvvzyy+xLTQnFq85E6cEu\nH0TpwX0v8wx5U+IjjzyCBx98EA0NDVi4cCEmTZqEH//4x6mojYiIiCjj8OpQ5hkyUFdWVuK5556D\n3++HYRhwOp2pqIuIiJKMB3Wi9GALdeYZMlBv2bIFzzzzDDo7O3ut/81vfpO0oii78IuFiIiyCU9m\nM8+Qgfqb3/wm1q5di/Hjx6eiHspC/GIhIiKisWzIQF1WVsZZESmpmKeJiIhoLBsyUN999924//77\nsXTpUihK99sZsilR2OODiIiIxrIhA/Wzzz4LANi6dWuv9QzURERERETnEajb2trwl7/8JRW1EBER\nERGNOUNO7LJw4UKsX78emqaloh7KQhzlg4iIsgmPe5lnyBbq9evXY926db3WSZKEffv2Ja0oIiIi\nokzF0a0yz5CBesOGDamog7IYv1iIiIhoLBswUD///PO444478Pjjj/f7+tq1a5NWFGUXXvoiIiKi\nsWzAPtRsNSQiIiIiGtqALdR33nknAKCiogKrVq3q9drvf//75FZFWYUnb0RElE14ZTbzDBiof/3r\nX8Pr9eIPf/gDmpqa4ut1Xccrr7yCu+66KyUFEhEREWUSNiRlngG7fEyaNKnf9WazGT/84Q+TVhAR\nERFRJmMLdeYZsIX6sssuw2WXXYbrrrsO06ZNS2VNlGV4ok5ERERj2ZDD5p06dQrf+MY30NnZ2esS\nxWPCKYgAAA1KSURBVFtvvZXUwih78ESdKD3YSkaUHuzykXmGDNTf+9738M1vfhMzZszgly8RERHR\nCDFPZZ4hA3VBQQEuu+yyhGwsGAzigQcewNmzZ+FwOPDoo4+isLCw13veeecdPPHEExBCoKqqCg8/\n/DD/8DIcT9SJ0oOtZEREiTHgTYld6uvr8YMf/AAbNmzARx99FP8Zjueeew4zZ87Es88+i5UrV+LJ\nJ5/s9brX68WPf/xj/PKXv8S6detQUVEBl8s1rG3R2MHzJaL0YGMFUXrwZDbzDNlCvXPnTgDA3r17\n4+skScJvfvObC97Y1q1bce+99wIAli9f3idQb9u2DTNnzsSjjz6KxsZGrF69uk8LNhEREdFYxpPZ\nzDNkoP7tb387rA9et24dnnnmmV7rioqKkJOTAwBwOBzweDy9Xne5XNi0aRNefPFF2O123HXXXait\nrcWUKVMG3E5BgR2KYhpWjTQ6FBU5UViYk+4yiLKO3W5GSQn3PaJUsVpVAIAsS9z3MsyQgbqpqQnf\n/va30dTUhN///vf4+te/ju9///uYMGHCoL+3evVqrF69ute6tWvXwufzAQB8Ph9yc3N7vZ6fn4/q\n6mqUlJQAABYuXIh9+/YNGqhdLv9Q/wk0yrW3+6DrarrLIMo6fn8YbW2eod9IRAkRDEYAALpucN8b\nowY6ERqyD/U///M/43Of+xzsdjuKi4tx44034sEHHxxWEXV1dXjnnXcAAO+++y7q6+t7vV5VVYWD\nBw+ivb0dmqZhx44dmD59+rC2RWMH+5IREVE2YZePzDNkoHa5XLjkkksARP8Abr/9dni93mFtbM2a\nNWhoaMCaNWvw/PPPY+3atQCAp59+Gm+99RaKiorw9a9/Hffeey9uv/12XHXVVZg5c+awtkVjB/M0\nUXrwZJaIKDGG7PJhtVpx+vTp+NnUli1bYDabh7Uxm82Gxx57rM/6e+65J758ww034IYbbhjW59PY\nxBN1ovRgKxlRevBkNvMMGagfeughfPGLX8SJEydwyy23oLOzE//+7/+eitqIiIiIiEa9IQN1dXU1\nXnjhBRw7dgy6rmPq1KnDbqEm6g9P1InSg61kRESJMWgf6vXr16OxsRGqquL48eP4+c9/jl/+8pfQ\nNC1V9RERERERjWoDBur/+q//wuOPP45QKIT9+/fj/vvvxxVXXAGfz4dHH300lTUSEREREY1aA3b5\neOmll/D888/DZrPhJz/5CS6//HKsXr0aQghcf/31qayRiIiIiGjUGrCFWpIk2Gw2AMCmTZtw6aWX\nxtcTERER0fAwS2WeAVuoTSYT3G43/H4/9u3bh2XLlgGIzpyoKEPey0hERERElBUGTMZf+MIXsHLl\nSmiahk9+8pMoLS3Fa6+9hp/97Gf4f+3dT2icZR7A8V+aNGmcRG1DN6uHyIKEeil2PHgorayha1HU\nUppqAxHUg4ISFQmCB+lCiYp4UYyCbEsV/FdEqN60iMU/eAi04sFFPHgSTLNxkxnTtCazB6EQ2urW\nZ/I+zsznc+rMmzC/y9v55n3ePw899FCRMwIANA132Gk+Fw3qnTt3xpYtW2J2djY2bdoUERGlUikO\nHDgQN954Y2EDAgA0E6d8NJ/fPHejv78/+vv7z72+6aabVn0gAABoJL95H2oAAOrLKR/NR1CTnZUv\nAKCRCWoAAEggqMnOyhcA0MgENdk55QMAaGSCGgAAEghqsnPKBwDQyAQ12TnlA4BW4sEuzUdQAwAU\nyH2om4+gJjv/rwAAjUxQk52VLwCgkQlqAABIIKgBACCBoCY7VzsDAI1MUAMAQAJBTXZuHwRAK7Ey\n23wENUCL8qUOUB+CGgCgQFZmm4+gBmhRvtQB6kNQk51lZ8jDvgdQH4IaAAASCGqys+wMedj3IA+r\nQ81HUAO0KF/qkIc/ZpuPoAYAgASCmuz8oQ55OEoGUB+CmuysOgMAjUxQAwAUyPULzUdQk51VZwCg\nkQlqAIACuX6h+QhqAABIIKgBACCBoCY712YAAI1MUAMAQAJBTXauzQAAGpmgBgCABIIaAKBAHuzS\nfAQ1AAAkENQAAAXyYJfmI6gBACCBoAYAgAQdRX7Y6dOnY3x8PGZmZqJUKsWzzz4bGzZsWPEzBw8e\njA8++CDa2triwQcfjB07dhQ5Ihm4NgMAaGSFHqF+8803Y3BwMN54443YtWtXTE5Ortg+NzcXr732\nWrz11ltx8ODBmJiYKHI8AAC4ZIUG9dTUVGzbti0iIrZv3x5ffPHFiu3d3d1x9dVXx8LCQiwsLLit\nTItwbQYA0MhW7ZSPI0eOxOHDh1e819fXF729vRERUSqVYn5+/rzfu+qqq+K2226LpaWleOCBB373\nc9avvyw6OtrrMzRZ9PWVoq+vN/cY0HJKpa7YuNG+B0Xp7u6MiIj29jX2vSazakE9PDwcw8PDK957\n+OGHo1qtRkREtVqNyy+/fMX248ePx48//hjHjh2LiIj7778/yuVybN68+aKfMzv7c50np2gzM9VY\nXu7MPQa0nGp1Maanzz+wAayOhYUzERGxtLRs32tQF/tDqNBTPsrlcnzyyScR8Ws833DDDSu2X3HF\nFbFu3bro7OyMrq6u6O3tjbm5uSJHBACAS1LoXT727dsXTzzxROzbty/Wrl0bzz//fEREHDp0KAYG\nBmJoaCg+//zz2Lt3b6xZsybK5XJs3bq1yBHJwKnyAEAjKzSou7u744UXXjjv/Xvvvffcv8fGxmJs\nbKzIsQAA4A/zYBeyc5cPAKCRCWoAAEggqAEAIIGgBgCABIIaAAASCGoAAEggqAEACtTmAQxNR1AD\nAEACQQ0AUKCaBzA0HUENAAAJBDUAACQQ1AAAkEBQAwAUyF0+mo+gBgCABIIaAKBA7vLRfAQ1AAAk\nENQAAJBAUAMAQAJBDQAACQQ1AAAkENQAAJBAUAMAQAJBDQAACQQ1AAAkENQAAJBAUAMAQAJBDQAA\nCQQ1AAAkENQAAJBAUAMAQAJBDQAACQQ1AAAkENQAAJBAUAMAQAJBDQAACQQ1AAAkENQAAJBAUAMA\nFKitrS33CNSZoAYAgASCGgCgAP39f42IiMHBTZknod46cg8AANAKbr75H3H27NnYvv3vuUehzgQ1\nAEABurq64o47duceg1XglA8AAEggqAEAIIGgBgCABIIaAAASCGoAAEggqAEAIIGgBgCABIKa7Nra\nck8AranNzgdQF1mC+sMPP4zHH3/8gtveeeed2L17d+zduzc+/vjjgicDAIBLU/iTEg8cOBCffvpp\nXHfddedtm56ejtdffz3efffdWFxcjJGRkdi6dWt0dnYWPSYFqtVyTwCtqWbnA6iLwo9Ql8vl2L9/\n/wW3ffXVV7Fly5bo7OyM3t7eGBgYiG+++abYASmcVWfIwykfAPWxakeojxw5EocPH17x3sTERNx6\n663x5ZdfXvB3KpVK9Pb2nntdKpWiUqn85uesX39ZdHS0pw9MNn19PbFhQ+/v/yBQV6VSV2zcaN8D\nSLVqQT08PBzDw8OX9Ds9PT1RrVbPva5WqysC+0JmZ3/+Q/Px53HqVCWWltbmHgNaTqVyOqan53OP\nAdAwLnYQ4k91l4/NmzfH1NRULC4uxvz8fHz33XcxODiYeyxWmVVnyMMpHwD1UfhFiRdy6NChGBgY\niKGhoRgdHY2RkZGo1Wrx2GOPRVdXV+7xAADgotpqDX6Zt+XKxvX00/+Mb7/9d7z00r+iu7s79zjQ\nMt5//714770j8eST++Paa60CAvy/LnbKh6Amm7m5/8bs7H/immv+lnsUaCnLy8sxM3MqNm78S+5R\nABqKoAYAgAQNcVEiAAA0GkENAAAJBDUAACQQ1AAAkEBQAwBAAkENAAAJBDUAACQQ1AAAkEBQAwBA\nAkENAAAJBDUAACQQ1AAAkKCtVqvVcg8BAACNyhFqAABIIKgBACCBoAYAgASCGgAAEghqAABIIKgB\nACCBoKZwy8vL8dRTT8Vdd90Vo6Oj8f333+ceCVrKyZMnY3R0NPcY0FLOnj0b4+PjMTIyEnv27Ilj\nx47lHok66sg9AK3no48+ijNnzsTbb78dJ06ciGeeeSZefvnl3GNBS3j11Vfj6NGj0d3dnXsUaClH\njx6NK6+8Mp577rn46aefYteuXTE0NJR7LOrEEWoKNzU1Fdu2bYuIiOuvvz6+/vrrzBNB6xgYGIgX\nX3wx9xjQcnbu3BmPPPJIRETUarVob2/PPBH1JKgpXKVSiZ6ennOv29vb45dffsk4EbSOW265JTo6\nLE5C0UqlUvT09ESlUomxsbF49NFHc49EHQlqCtfT0xPVavXc6+XlZV/wADS9H374Ie6555648847\n4/bbb889DnUkqClcuVyO48ePR0TEiRMnYnBwMPNEALC6Tp06Fffdd1+Mj4/Hnj17co9DnTksSOF2\n7NgRn332Wdx9991Rq9ViYmIi90gAsKpeeeWVmJubi8nJyZicnIyIXy8SXrduXebJqIe2Wq1Wyz0E\nAAA0Kqd8AABAAkENAAAJBDUAACQQ1AAAkEBQAwBAAkENAAAJBDUAACQQ1AAAkOB/a7uTC78prUMA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e8c443780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.violinplot(x='problem_type', y='sentiment_score', data=df_topics).set(xlabel='', ylabel='Sentiment Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "sns.jointplot(distplot.stars_dif, distplot.sentiment_score, kind = 'kde', cmap=\"Reds\", shade=True, shade_lowest=False)\n",
    "sns.plt.ylim(-0.25,0)\n",
    "sns.plt.xlim((-4),2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}