YELP Text Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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",
    "\n",
    "import os\n",
    "import codecs\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "import warnings\n",
    "import _pickle as pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (0,1,6,11,12,13,20) 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, 27)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data.csv\", encoding='ISO-8859-1')\n",
    "np.shape(data) #(2043401, 27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.drop('Unnamed: 0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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</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>-112.365</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</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>-112.365</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</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>-112.365</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</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>-112.365</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</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>-112.365</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  2017-06-03     0.0   0.0    2016-06-17      ...         AZ   \n",
       "1     4  2015-03-26     1.0   0.0    2012-09-28      ...         AZ   \n",
       "2     5  2012-12-30     2.0   1.0    2010-10-19      ...         AZ   \n",
       "3     3  2009-01-12     1.0   1.0    2008-11-20      ...         AZ   \n",
       "4     3  2015-07-11     0.0   0.0    2014-08-03      ...         AZ   \n",
       "\n",
       "  postal_code   latitude longitude business_avg_stars review_count_y is_open  \\\n",
       "0     85374.0  33.638228  -112.365                3.5           92.0     1.0   \n",
       "1     85374.0  33.638228  -112.365                3.5           92.0     1.0   \n",
       "2     85374.0  33.638228  -112.365                3.5           92.0     1.0   \n",
       "3     85374.0  33.638228  -112.365                3.5           92.0     1.0   \n",
       "4     85374.0  33.638228  -112.365                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",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filtered = textPreProcess(data['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sentimentScore(data, filtered):\n",
    "    \n",
    "    neg_file = open(\"negative-words.txt\", \"r\")\n",
    "    neg_dict = neg_file.read().split('\\n')\n",
    "    neg_file.close()\n",
    "\n",
    "    pos_file = open(\"positive-words.txt\", \"r\")\n",
    "    pos_dict = pos_file.read().split('\\n')\n",
    "    pos_file.close()\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": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2h 40min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "scored_df = sentimentScore(data, filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scored_df.to_csv('scored_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (1,6,11,12,13,20) 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, 29)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored_df = pd.read_csv(\"scored_df.csv\", encoding='ISO-8859-1')\n",
    "np.shape(scored_df) #(2043401, 29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr_df = scored_df.loc[:,['stars', 'sentiment_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scored_df = scored_df.dropna(subset=['stars'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scored_df = scored_df[scored_df.stars != '2013-06-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scored_df = scored_df.drop('Unnamed: 0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scored_df['average_stars'] = scored_df['average_stars'].apply(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.752122820847011"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored_df['average_stars'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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>business_avg_stars</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>2.043399e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.042974e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2043399.0</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "      <td>2.043399e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.748968e+00</td>\n",
       "      <td>9.164417e-01</td>\n",
       "      <td>5.244375e-01</td>\n",
       "      <td>7.397933e+04</td>\n",
       "      <td>3.598662e+01</td>\n",
       "      <td>3.751935e+00</td>\n",
       "      <td>5.253172e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.154558e-03</td>\n",
       "      <td>-2.966381e-03</td>\n",
       "      <td>1.053997e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.358269e+00</td>\n",
       "      <td>2.498583e+00</td>\n",
       "      <td>2.101433e+00</td>\n",
       "      <td>2.428910e+04</td>\n",
       "      <td>2.666742e+00</td>\n",
       "      <td>5.842759e-01</td>\n",
       "      <td>8.720526e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.166744e+00</td>\n",
       "      <td>1.243536e+00</td>\n",
       "      <td>1.075339e-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>2.224000e+03</td>\n",
       "      <td>3.313739e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</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>3.500000e+00</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>4.000000e+00</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>4.000000e+00</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>4.650400e+01</td>\n",
       "      <td>5.000000e+00</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  2.043399e+06  2.043399e+06  2.043399e+06  2.042974e+06  2.043399e+06   \n",
       "mean   3.748968e+00  9.164417e-01  5.244375e-01  7.397933e+04  3.598662e+01   \n",
       "std    1.358269e+00  2.498583e+00  2.101433e+00  2.428910e+04  2.666742e+00   \n",
       "min    1.000000e+00  0.000000e+00  0.000000e+00  2.224000e+03  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  4.650400e+01   \n",
       "\n",
       "       business_avg_stars  review_count_y    is_open     stars_dif  \\\n",
       "count        2.043399e+06    2.043399e+06  2043399.0  2.043399e+06   \n",
       "mean         3.751935e+00    5.253172e+02        1.0 -3.154558e-03   \n",
       "std          5.842759e-01    8.720526e+02        0.0  1.166744e+00   \n",
       "min          1.000000e+00    3.000000e+00        1.0 -3.910000e+00   \n",
       "25%          3.500000e+00    1.030000e+02        1.0 -6.700000e-01   \n",
       "50%          4.000000e+00    2.480000e+02        1.0  1.100000e-01   \n",
       "75%          4.000000e+00    5.610000e+02        1.0  8.200000e-01   \n",
       "max          5.000000e+00    6.979000e+03        1.0  3.850000e+00   \n",
       "\n",
       "       bus_stars_dif  sentiment_score  \n",
       "count   2.043399e+06     2.043399e+06  \n",
       "mean   -2.966381e-03     1.053997e-01  \n",
       "std     1.243536e+00     1.075339e-01  \n",
       "min    -4.000000e+00    -1.000000e+00  \n",
       "25%    -1.000000e+00     3.773585e-02  \n",
       "50%     5.000000e-01     9.722222e-02  \n",
       "75%     1.000000e+00     1.666667e-01  \n",
       "max     4.000000e+00     1.000000e+00  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>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</th>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>...</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "      <td>228344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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</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</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</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      228344   228344     228344  228344       228344  228344  228344   \n",
       "2      190147   190147     190147  190147       190147  190147  190147   \n",
       "3      265286   265286     265286  265286       265286  265286  265286   \n",
       "4      541968   541968     541968  541968       541968  541968  541968   \n",
       "5      817654   817654     817654  817654       817654  817654  817654   \n",
       "\n",
       "         cool  yelping_since  review_count_x       ...         latitude  \\\n",
       "stars                                              ...                    \n",
       "1      228344         228344          228344       ...           228344   \n",
       "2      190147         190147          190147       ...           190147   \n",
       "3      265286         265286          265286       ...           265286   \n",
       "4      541968         541968          541968       ...           541968   \n",
       "5      817654         817654          817654       ...           817654   \n",
       "\n",
       "       longitude  business_avg_stars  review_count_y  is_open  categories  \\\n",
       "stars                                                                       \n",
       "1         228344              228344          228344   228344      228344   \n",
       "2         190147              190147          190147   190147      190147   \n",
       "3         265286              265286          265286   265286      265286   \n",
       "4         541968              541968          541968   541968      541968   \n",
       "5         817654              817654          817654   817654      817654   \n",
       "\n",
       "       stars_dif  bus_stars_dif  filtered_text  sentiment_score  \n",
       "stars                                                            \n",
       "1         228344         228344         228344           228344  \n",
       "2         190147         190147         190147           190147  \n",
       "3         265286         265286         265286           265286  \n",
       "4         541968         541968         541968           541968  \n",
       "5         817654         817654         817654           817654  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored_df.groupby('stars').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scored_df['stars'] = scored_df['stars'].apply(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.567907522791\n",
      "0.454066529438\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": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distplot3 = scored_df.loc[:,['sentiment_score', 'stars_dif', 'stars']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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": [
       "(-4, 2)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f72f2f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qITPVQVFpNaYZ/tnlFV4ix6FzTTHm1sJYlyESlzplJlFV28D2vZ6wb1vhJdJO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f72fd7dd8>"
      ]
     },
     "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": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'stars'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-239-47a63956fc8d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"darkgrid\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"axes.facecolor\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\".9\"\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdistplot3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msentiment_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'kde'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Reds\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshade\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshade_lowest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;31m#sns.plt.ylim(-0.25,0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   2968\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2969\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2970\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2971\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2972\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'stars'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x270f5154a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "sns.jointplot(distplot3.stars, 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": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.57184624524878747, pvalue=0.0)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy as sp\n",
    "from scipy import stats\n",
    "sp.stats.spearmanr(scored_df['stars'], scored_df['sentiment_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f727a9828>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yLEuS5PF4NDo6qkgkIp/PN/l9Ho9HkUhkyvFPrv3N7fzfHL9w4YLcbrcWLFgw5fjo6Oi0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f3cad1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_df = scored_df.groupby('stars')['is_open'].sum()\n",
    "bar_df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.4, 0.4)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4nq1/d5MkacmCosmPzdEO+byeJFSVmRjLxBr5fL72kgXa8nKtDpzo1k1XL7K4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\nRCRs/AEV43a8AK/XbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f3540fb00>"
      ]
     },
     "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": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(223487, 28)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scored_df = pd.read_csv(\"scored_df.csv\", encoding='ISO-8859-1')\n",
    "neg_scores = scored_df[scored_df['sentiment_score'] < 0]\n",
    "np.shape(neg_scores) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "neg_scores['stars'] = neg_scores['stars'].apply(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.14814724011469252, pvalue=0.0)\n",
      "SpearmanrResult(correlation=0.046634209769850277, pvalue=8.0176788335912573e-108)\n"
     ]
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 110,
   "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>business_avg_stars</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>223487.000000</td>\n",
       "      <td>223487.000000</td>\n",
       "      <td>223487.000000</td>\n",
       "      <td>223443.000000</td>\n",
       "      <td>223487.000000</td>\n",
       "      <td>223487.000000</td>\n",
       "      <td>223487.000000</td>\n",
       "      <td>223487.0</td>\n",
       "      <td>223487.000000</td>\n",
       "      <td>223487.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.863621</td>\n",
       "      <td>0.961161</td>\n",
       "      <td>0.308461</td>\n",
       "      <td>74167.992625</td>\n",
       "      <td>36.005608</td>\n",
       "      <td>3.370149</td>\n",
       "      <td>432.892092</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.292197</td>\n",
       "      <td>-1.506528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.166688</td>\n",
       "      <td>2.203870</td>\n",
       "      <td>1.449252</td>\n",
       "      <td>24142.488977</td>\n",
       "      <td>2.647408</td>\n",
       "      <td>0.698158</td>\n",
       "      <td>790.342587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.164313</td>\n",
       "      <td>1.164704</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>1.000000</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>33.567812</td>\n",
       "      <td>3.000000</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>36.072381</td>\n",
       "      <td>3.500000</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>4.000000</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>5.000000</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  223487.000000  223487.000000  223487.000000  223443.000000   \n",
       "mean        1.863621       0.961161       0.308461   74167.992625   \n",
       "std         1.166688       2.203870       1.449252   24142.488977   \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  business_avg_stars  review_count_y   is_open  \\\n",
       "count  223487.000000       223487.000000   223487.000000  223487.0   \n",
       "mean       36.005608            3.370149      432.892092       1.0   \n",
       "std         2.647408            0.698158      790.342587       0.0   \n",
       "min        33.137390            1.000000        3.000000       1.0   \n",
       "25%        33.567812            3.000000       67.000000       1.0   \n",
       "50%        36.072381            3.500000      178.000000       1.0   \n",
       "75%        36.148619            4.000000      435.000000       1.0   \n",
       "max        44.992140            5.000000     6979.000000       1.0   \n",
       "\n",
       "           stars_dif  bus_stars_dif  \n",
       "count  223487.000000  223487.000000  \n",
       "mean       -1.292197      -1.506528  \n",
       "std         1.164313       1.164704  \n",
       "min        -3.890000      -4.000000  \n",
       "25%        -2.190000      -2.500000  \n",
       "50%        -1.440000      -1.500000  \n",
       "75%        -0.360000      -1.000000  \n",
       "max         3.560000       3.500000  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_scores.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.07000000e+02,   2.76300000e+03,   9.00700000e+03,\n",
       "          1.20450000e+04,   1.76710000e+04,   1.92010000e+04,\n",
       "          2.43140000e+04,   2.03640000e+04,   2.10600000e+04,\n",
       "          1.62360000e+04,   1.47790000e+04,   1.16180000e+04,\n",
       "          7.12200000e+03,   2.74620000e+04,   6.31700000e+03,\n",
       "          4.41200000e+03,   3.61600000e+03,   2.53100000e+03,\n",
       "          1.34000000e+03,   6.99000000e+02,   2.83000000e+02,\n",
       "          1.28000000e+02,   7.30000000e+01,   2.80000000e+01,\n",
       "          1.10000000e+01]),\n",
       " array([-3.89 , -3.592, -3.294, -2.996, -2.698, -2.4  , -2.102, -1.804,\n",
       "        -1.506, -1.208, -0.91 , -0.612, -0.314, -0.016,  0.282,  0.58 ,\n",
       "         0.878,  1.176,  1.474,  1.772,  2.07 ,  2.368,  2.666,  2.964,\n",
       "         3.262,  3.56 ]),\n",
       " <a list of 25 Patch objects>)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ag8QzAAAM2srrPLPDrK4eXPQQAADOClaeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e866ace80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(neg_scores['stars_dif'], bins=25, color=\"#666699\")  #.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   2.00000000e+00,\n",
       "          2.00000000e+00,   4.00000000e+00,   6.00000000e+00,\n",
       "          6.00000000e+00,   2.30000000e+01,   7.00000000e+00,\n",
       "          7.90000000e+01,   7.70000000e+01,   1.22000000e+02,\n",
       "          3.75000000e+02,   5.37000000e+02,   9.45000000e+02,\n",
       "          1.88900000e+03,   2.79700000e+03,   7.60200000e+03,\n",
       "          1.41430000e+04,   3.19980000e+04,   6.82080000e+04,\n",
       "          9.46560000e+04]),\n",
       " array([-1.        , -0.96007968, -0.92015936, -0.88023904, -0.84031873,\n",
       "        -0.80039841, -0.76047809, -0.72055777, -0.68063745, -0.64071713,\n",
       "        -0.60079681, -0.56087649, -0.52095618, -0.48103586, -0.44111554,\n",
       "        -0.40119522, -0.3612749 , -0.32135458, -0.28143426, -0.24151394,\n",
       "        -0.20159363, -0.16167331, -0.12175299, -0.08183267, -0.04191235,\n",
       "        -0.00199203]),\n",
       " <a list of 25 Patch objects>)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j7ap6IJMI3pnk7jHGG1X1YJJHquq5JMcyeZFgknwqyReT7Mrkt218dUOfFQAAbIJZZ54/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ec3dd6048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(neg_scores['sentiment_score'], bins=25, color=\"#666699\")  #.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec3e02cf8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j3sTEQsON7WYhfLutDjPAXOX2NJAGGnNKV5IkqQXMZ3NkVwsM7dLBFVWt1OO73RnfXwP+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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ec3e021d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.distplot(neg_scores['sentiment_score'], label='Sentiment Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f1bcf1240>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SHU8CEA7m0w6hnmARkdkUgkUW0Vh8ih8duERsIsWWtXU8vmsFlQEvd62opirg4fkjXYzE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\nSZJa52+adOpTeEza6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f1bcf12b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.distplot(neg_scores['stars_dif'], label='Stars_Dif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "standardizer = lambda x: (2 * ((x - min(x)) / (max(x) - min(x))) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(223487, 3)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distplot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distplot['stars_norm'] = distplot[['stars_dif']].apply(standardizer, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>stars_norm</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>-0.672483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.040000</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-0.527517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.040000</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.084564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.022989</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-0.393289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.057692</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-0.280537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment_score  stars_dif  stars_norm\n",
       "0        -0.033333      -2.67   -0.672483\n",
       "1        -0.040000      -2.13   -0.527517\n",
       "2        -0.040000      -0.48   -0.084564\n",
       "3        -0.022989      -1.63   -0.393289\n",
       "4        -0.057692      -1.21   -0.280537"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distplot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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 0x26ec3dbbdd8>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AuUfwBYBeaGgwVF5uyczRu2n6OARfAMg9gi8AZKmtTWprM1RWlrvBu7Q6AIBz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\npVpDav7RpXINHabyx59V5St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Tv6jbjjpNkuRZOE+yLHkSEfljIRVEG1XStkmloQ0qbdug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\nBQl8hRBCCCFEWJDAVwghhBBChIX/B5Yk2PyoiZ93AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e9ec18400>"
      ]
     },
     "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(distplot['stars_norm'], color=\"r\")\n",
    "ax = sns.distplot(distplot['sentiment_score'], color=\"b\")\n",
    "\n",
    "# Add labels to the plot\n",
    "red = sns.color_palette(\"Reds\")[-2]\n",
    "blue = sns.color_palette(\"Blues\")[-2]\n",
    "ax.text(0.05, 8.0, \"Sentiment Score\", size=16, color=blue)\n",
    "ax.text(0.15, 1.5, \"Stars_Norm\", size=16, color=red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26e89b80080>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JIcRAqhWwtYLs1JBK5LNmExIJPL1rGOO+EJKp9C36Xq8LT3x2A1hLZf+U6mnb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\nFov2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ec26102b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(neg_scores['sentiment_score'], neg_scores['stars_dif'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26ed12f2c50>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sMBkEKHF10v+7nSKMRgEJNZnSx9iZMxiAsSkODAJgt5ggCBrUa+FZRCPuuLkZ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\nTA3ZWcHBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e8c593470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(neg_scores['sentiment_score'], neg_scores['review_count_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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 0x26e903abda0>,\n",
       " <matplotlib.text.Text at 0x26f1a7016d8>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dhk3k5KRnCF0qXClclxYuItee2pVCk4H6YqkO1KoaY2D4HAPelilD9Lp1G8jL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\nc5GVTiKuVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f2a8da438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.violinplot(x='stars', y='sentiment_score', data=neg_scores).set(xlabel='', ylabel='Sentiment Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec2610e48>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u15jjFzo3KSlp3LWcO3c+ki0YaWBgMNZLmDJSUlzMI8qYcfQx48kx3eZ8oRcgEd1+z8rK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\nAAAM8T9Eoe/EAwxzVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ec2634860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_df = neg_scores.groupby('stars')['is_open'].sum()\n",
    "bar_df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distplot = neg_scores.loc[:,['sentiment_score', 'stars_dif']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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": [
       "(-4, 2)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f3cb3f588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ICy+8EDfffLPv9e+++w7XXHMNhg0bhq1bt2LYsGGhfRDUCcNLAWHPUxiNm4QR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f727a92b0>"
      ]
     },
     "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": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['funny',\n",
       " 'user_id',\n",
       " 'review_id',\n",
       " 'text',\n",
       " 'business_id',\n",
       " 'stars',\n",
       " 'date',\n",
       " 'useful',\n",
       " 'cool',\n",
       " 'yelping_since',\n",
       " 'review_count_x',\n",
       " 'fans',\n",
       " 'average_stars',\n",
       " 'name',\n",
       " 'neighborhood',\n",
       " 'city',\n",
       " 'state',\n",
       " 'postal_code',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'business_avg_stars',\n",
       " 'review_count_y',\n",
       " 'is_open',\n",
       " 'categories',\n",
       " 'stars_dif',\n",
       " 'bus_stars_dif',\n",
       " 'filtered_text',\n",
       " 'sentiment_score']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_scores.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>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</th>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>...</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "      <td>120180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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</th>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>...</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "      <td>25980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>...</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "      <td>14483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>...</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</td>\n",
       "      <td>11585</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      120180   120180     120180  120180       120180  120180  120180   \n",
       "2       51259    51259      51259   51259        51259   51259   51259   \n",
       "3       25980    25980      25980   25980        25980   25980   25980   \n",
       "4       14483    14483      14483   14483        14483   14483   14483   \n",
       "5       11585    11585      11585   11585        11585   11585   11585   \n",
       "\n",
       "         cool  yelping_since  review_count_x       ...         latitude  \\\n",
       "stars                                              ...                    \n",
       "1      120180         120180          120180       ...           120180   \n",
       "2       51259          51259           51259       ...            51259   \n",
       "3       25980          25980           25980       ...            25980   \n",
       "4       14483          14483           14483       ...            14483   \n",
       "5       11585          11585           11585       ...            11585   \n",
       "\n",
       "       longitude  business_avg_stars  review_count_y  is_open  categories  \\\n",
       "stars                                                                       \n",
       "1         120180              120180          120180   120180      120180   \n",
       "2          51259               51259           51259    51259       51259   \n",
       "3          25980               25980           25980    25980       25980   \n",
       "4          14483               14483           14483    14483       14483   \n",
       "5          11585               11585           11585    11585       11585   \n",
       "\n",
       "       stars_dif  bus_stars_dif  filtered_text  sentiment_score  \n",
       "stars                                                            \n",
       "1         120180         120180         120180           120180  \n",
       "2          51259          51259          51259            51259  \n",
       "3          25980          25980          25980            25980  \n",
       "4          14483          14483          14483            14483  \n",
       "5          11585          11585          11585            11585  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_scores.groupby('stars').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(neg_scores['stars'].iloc[223486])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neg_scores.to_csv('neg_scores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neg_scores = pd.read_csv(\"neg_scores.csv\", encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 24,
   "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'] #'service', \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.  Stream\n",
    "    # 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\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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\n",
    "    # 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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
      "Wall time: 19min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "bigram_model, trigram_model, trigram_dictionary = reviewPreProcess(neg_scores['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "trigram_bow_corpus, lda = LDA_Model(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "term                 frequency\n",
      "\n",
      "eat                  0.013\n",
      "like                 0.011\n",
      "taste                0.010\n",
      "chicken              0.010\n",
      "service              0.008\n",
      "$                    0.006\n",
      "try                  0.006\n",
      "fry                  0.006\n",
      "restaurant           0.006\n",
      "meat                 0.005\n",
      "dish                 0.005\n",
      "sauce                0.005\n",
      "pizza                0.005\n",
      "meal                 0.005\n",
      "cold                 0.005\n",
      "bland                0.005\n",
      "salad                0.005\n",
      "think                0.005\n",
      "burger               0.005\n",
      "flavor               0.004\n",
      "look                 0.004\n",
      "cheese               0.004\n",
      "dry                  0.004\n",
      "star                 0.004\n",
      "sandwich             0.004\n"
     ]
    }
   ],
   "source": [
    "explore_topic(topic_number=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "term                 frequency\n",
      "\n",
      "$                    0.011\n",
      "restaurant           0.009\n",
      "try                  0.008\n",
      "service              0.007\n",
      "like                 0.007\n",
      "want                 0.006\n",
      "menu                 0.006\n",
      "price                0.006\n",
      "drink                0.006\n",
      "think                0.006\n",
      "bar                  0.005\n",
      "know                 0.005\n",
      "people               0.004\n",
      "eat                  0.004\n",
      "thing                0.004\n",
      "vegas                0.004\n",
      "find                 0.004\n",
      "charge               0.004\n",
      "tell                 0.004\n",
      "day                  0.004\n",
      "ask                  0.003\n",
      "way                  0.003\n",
      "great                0.003\n",
      "look                 0.003\n",
      "buffet               0.003\n"
     ]
    }
   ],
   "source": [
    "explore_topic(topic_number=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "term                 frequency\n",
      "\n",
      "service              0.019\n",
      "wait                 0.016\n",
      "ask                  0.015\n",
      "table                0.014\n",
      "minute               0.012\n",
      "tell                 0.011\n",
      "manager              0.009\n",
      "server               0.009\n",
      "customer             0.008\n",
      "drink                0.008\n",
      "want                 0.007\n",
      "leave                0.007\n",
      "restaurant           0.007\n",
      "people               0.006\n",
      "experience           0.006\n",
      "waitress             0.006\n",
      "eat                  0.006\n",
      "know                 0.006\n",
      "sit                  0.006\n",
      "seat                 0.005\n",
      "like                 0.005\n",
      "rude                 0.005\n",
      "check                0.005\n",
      "walk                 0.005\n",
      "staff                0.005\n"
     ]
    }
   ],
   "source": [
    "explore_topic(topic_number=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare for LDA visualization\n",
    "\n",
    "trigram_dictionary = Dictionary.load('./models/trigram_dict_all_neg.dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\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_el100426750043487205740650985\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el100426750043487205740650985_data = {\"mdsDat\": {\"Freq\": [41.08192964673989, 33.65073125335054, 25.26733909990958], \"cluster\": [1, 1, 1], \"topics\": [1, 2, 3], \"x\": [-0.10326770499968935, 0.10197895261873534, 0.0012887523809539951], \"y\": [-0.02700021659589711, -0.028036958801456232, 0.055037175397353366]}, \"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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\"customer\", \"customer\", \"dance\", \"dance\", \"dance\", \"dance_floor\", \"dance_floor\", \"dance_floor\", \"dark_wood\", \"das\", \"das\", \"das\", \"day\", \"day\", \"day\", \"de\", \"de\", \"de\", \"der\", \"der\", \"der\", \"dim_sum\", \"dim_sum\", \"dim_sum\", \"discourteous\", \"dish\", \"dish\", \"dish\", \"dort\", \"drink\", \"drink\", \"drink\", \"drive_thru_speaker\", \"dry\", \"dry\", \"dry\", \"dumbfound\", \"dysfunctional\", \"dysfunctional\", \"earl\", \"earl\", \"earl\", \"eat\", \"eat\", \"eat\", \"egg_drop\", \"egg_drop\", \"egg_drop\", \"egg_roll\", \"egg_roll\", \"egg_roll\", \"ein\", \"ein\", \"eine\", \"einen\", \"einen\", \"employee\", \"employee\", \"employee\", \"en\", \"en\", \"en\", \"et\", \"et\", \"etwa\", \"experience\", \"experience\", \"experience\", \"extremely_rude\", \"extremely_rude\", \"extremely_rude\", \"fatty\", \"fatty\", \"fatty\", \"finally\", \"finally\", \"finally\", \"find\", \"find\", \"find\", \"fish\", \"fish\", \"fish\", \"flavor\", \"flavor\", \"flavor\", \"flavorless\", \"flavorless\", \"flavorless\", \"fried\", \"fried\", \"fried\", \"fried_rice\", \"fried_rice\", \"fried_rice\", \"fry\", \"fry\", \"fry\", \"f\\u00fcr\", \"game\", \"game\", \"game\", \"greasy\", \"greasy\", \"greasy\", \"great\", \"great\", \"great\", \"greet\", \"greet\", \"greet\", \"gross\", \"gross\", \"gross\", \"groupon\", \"groupon\", \"groupon\", \"hang_up\", \"hang_up\", \"hang_up\", \"hier\", \"hier\", \"hier\", \"high\", \"high\", \"high\", \"hob\", \"hob\", \"hollywood\", \"hollywood\", \"horrible\", \"horrible\", \"horrible\", \"host_stand\", \"host_stand\", \"host_stand\", \"hostess\", \"hostess\", \"hostess\", \"hostess_stand\", \"hostess_stand\", \"hostess_stand\", \"hot_fudge\", \"hot_fudge\", \"hot_fudge\", \"huge_inconvenience\", \"huge_inconvenience\", \"hunter\", \"i.d\", \"ice_cream\", \"ice_cream\", \"ice_cream\", \"ich\", \"ich\", \"idli\", \"ignore\", \"ignore\", \"ignore\", \"india\", \"india\", \"india\", \"indian\", \"indian\", \"indian\", \"indians\", \"indians\", \"indians\", \"ireland\", \"ist\", \"ist\", \"ist\", \"julie\", \"karaoke\", \"karaoke\", \"karaoke\", \"know\", \"know\", \"know\", \"le\", \"le\", \"le\", \"leave\", \"leave\", \"leave\", \"like\", \"like\", \"like\", \"little\", \"little\", \"little\", \"local\", \"local\", \"local\", \"long\", \"long\", \"long\", \"look\", \"look\", \"look\", \"mais\", \"mais\", \"make_amend\", \"make_amend\", \"make_eye_contact\", \"make_eye_contact\", \"make_eye_contact\", \"manager\", \"manager\", \"manager\", \"meal\", \"meal\", \"meal\", \"meat\", \"meat\", \"meat\", \"menu\", \"menu\", \"menu\", \"min\", \"min\", \"min\", \"minuet\", \"minuet\", \"minuet\", \"minute\", \"minute\", \"minute\", \"minute_later\", \"minute_later\", \"minute_later\", \"minutes\", \"minutes\", \"mit\", \"mit\", \"mit\", \"mongolian_beef\", \"mongolian_beef\", \"mongolian_beef\", \"mushy\", \"mushy\", \"mushy\", \"music\", \"music\", \"music\", \"nicht\", \"nicht\", \"nicht\", \"night\", \"night\", \"night\", \"no_apology\", \"no_apology\", \"no_apology\", \"noodle\", \"noodle\", \"noodle\", \"nur\", \"oil\", \"oil\", \"oil\", \"ok\", \"ok\", \"ok\", \"orange_chicken\", \"orange_chicken\", \"orange_chicken\", \"overheard\", \"pad_thai\", \"pad_thai\", \"pad_thai\", \"palace_station\", \"palace_station\", \"palace_station\", \"pan_roast\", \"pan_roast\", \"pan_roast\", \"para\", \"para\", \"parking\", \"parking\", \"parking\", \"pay\", \"pay\", \"pay\", \"people\", \"people\", \"people\", \"pero\", \"pero\", \"pero\", \"phone\", \"phone\", \"phone\", \"piece\", \"piece\", \"piece\", \"pizza\", \"pizza\", \"pizza\", \"plate\", \"plate\", \"plate\", \"play\", \"play\", \"play\", \"podium\", \"podium\", \"pork\", \"pork\", \"pork\", \"postmates\", \"prep_area\", \"prep_area\", \"prep_area\", \"price\", \"price\", \"price\", \"pubic_hair\", \"pull_forward\", \"pull_forward\", \"quality\", \"quality\", \"quality\", \"que\", \"que\", \"que\", \"ready\", \"ready\", \"ready\", \"register\", \"register\", \"register\", \"reservation\", \"reservation\", \"reservation\", \"restaurant\", \"restaurant\", \"restaurant\", \"review\", \"review\", \"review\", \"rice\", \"rice\", \"rice\", \"roast_pork\", \"roast_pork\", \"robin\", \"robin\", \"rude\", \"rude\", \"rude\", \"rude_tone\", \"rude_tone\", \"rude_tone\", \"rudely\", \"rudely\", \"rudely\", \"salad\", \"salad\", \"salad\", \"salmon\", \"salmon\", \"salmon\", \"salt\", \"salt\", \"salt\", \"salty\", \"salty\", \"salty\", \"sambar\", \"sambar\", \"sandwich\", \"sandwich\", \"sandwich\", \"sauce\", \"sauce\", \"sauce\", \"scallop\", \"scallop\", \"scallop\", \"schon\", \"seasoning\", \"seasoning\", \"seasoning\", \"seat\", \"seat\", \"seat\", \"sehr\", \"sehr\", \"sehr\", \"selection\", \"selection\", \"selection\", \"serve\", \"serve\", \"serve\", \"server\", \"server\", \"server\", \"service\", \"service\", \"service\", \"shrimp\", \"shrimp\", \"shrimp\", \"shuffleboard\", \"sich\", \"silver_wear\", \"silver_wear\", \"sind\", \"sit\", \"sit\", \"sit\", \"small\", \"small\", \"small\", \"smoke\", \"smoke\", \"smoke\", \"sofa\", \"soggy\", \"soggy\", \"soggy\", \"song\", \"song\", \"song\", \"soup\", \"soup\", \"soup\", \"speak\", \"speak\", \"speak\", \"spicy\", \"spicy\", \"spicy\", \"sport\", \"sport\", \"sport\", \"sport_bar\", \"sport_bar\", \"sport_bar\", \"sport_game\", \"sport_game\", \"spot\", \"spot\", \"spot\", \"squid\", \"squid\", \"squid\", \"staff\", \"staff\", \"staff\", \"stand_around\", \"stand_around\", \"stand_around\", \"star\", \"star\", \"star\", \"steak\", \"steak\", \"steak\", \"strawberry_shortcake\", \"strawberry_shortcake\", \"supervisor\", \"supervisor\", \"supervisor\", \"sushi\", \"sushi\", \"sushi\", \"table\", \"table\", \"table\", \"taco_shop\", \"taco_shop\", \"taco_shop\", \"tacos_el_gordo\", \"tacos_el_gordo\", \"tacos_el_gordo\", \"talk_amongst\", \"talk_amongst\", \"tao\", \"tao\", \"tao\", \"taste\", \"taste\", \"taste\", \"tasteless\", \"tasteless\", \"tasteless\", \"tell\", \"tell\", \"tell\", \"thai\", \"thai\", \"thai\", \"thing\", \"thing\", \"thing\", \"think\", \"think\", \"think\", \"tilted_kilt\", \"travis\", \"try\", \"try\", \"try\", \"tv\", \"tv\", \"tv\", \"una\", \"una\", \"und\", \"und\", \"und\", \"und_die\", \"une\", \"une\", \"unprofessional\", \"unprofessional\", \"unprofessional\", \"uns\", \"upsell\", \"upsell\", \"upsell\", \"urgency\", \"urgency\", \"urgency\", \"use\", \"use\", \"use\", \"vanilla\", \"vanilla\", \"vanilla\", \"vegas\", \"vegas\", \"vegas\", \"vegetable\", \"vegetable\", \"vegetable\", \"venue\", \"venue\", \"venue\", \"visit\", \"visit\", \"visit\", \"waffle_cone\", \"wait\", \"wait\", \"wait\", \"waiter\", \"waiter\", \"waiter\", \"waiting_list\", \"waiting_list\", \"waitress\", \"waitress\", \"waitress\", \"walk\", \"walk\", \"walk\", \"walk_away\", \"walk_away\", \"walk_away\", \"want\", \"want\", \"want\", \"way\", \"way\", \"way\", \"weary_traveler\", \"weary_traveler\", \"wie\", \"wie\", \"wine\", \"wine\", \"wine\", \"wir\", \"wir\", \"wonton\", \"wonton\", \"wonton\", \"yell\", \"yell\", \"yell\", \"zu\", \"zu\", \"zu\", \"zum\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 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_el100426750043487205740650985\", ldavis_el100426750043487205740650985_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_el100426750043487205740650985\", ldavis_el100426750043487205740650985_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_el100426750043487205740650985\", ldavis_el100426750043487205740650985_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 32,
     "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": [
    "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": [
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       "    .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",
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       "      <th>stars_dif</th>\n",
       "      <th>bus_stars_dif</th>\n",
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       "      <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",
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       "      <td>['Fast Food', 'Gluten-Free', 'Asian Fusion', '...</td>\n",
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       "      <td>-2.5</td>\n",
       "      <td>['wish', 'could', 'give', '1', '5', 'stars', '...</td>\n",
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       "      <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",
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       "    <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",
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       "      <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": {
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IxzQYSRYcc3YQLEkldOHQ6fDAZWQsi2NRKjHOpWuV/W5WEwXBhJCGMjoeZQZa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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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\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
}