Polarity, Sentiment and Word Graph (For a term).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied (use --upgrade to upgrade): twitter in c:\\users\\shraddha somani\\anaconda3\\lib\\site-packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 8.1.2, however version 9.0.1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied (use --upgrade to upgrade): textblob in c:\\users\\shraddha somani\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied (use --upgrade to upgrade): nltk>=3.1 in c:\\users\\shraddha somani\\anaconda3\\lib\\site-packages (from textblob)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 8.1.2, however version 9.0.1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "import pip\n",
    "pip.main([\"install\",\"twitter\"])\n",
    "pip.main([\"install\",\"textblob\"])\n",
    "from textblob import TextBlob\n",
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "import twitter\n",
    "from twitter import Twitter \n",
    "from twitter import OAuth \n",
    "from twitter import TwitterHTTPError \n",
    "from twitter import TwitterStream\n",
    "from pandas.io.json import json_normalize\n",
    "from matplotlib import pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Authenticate\n",
    "ACCESS_TOKEN = '3151548164-KGXKM6vZipCfBfIA16bUxBaNhIcg7CmfiYb6iWU'\n",
    "ACCESS_SECRET = '8qKGbi5JRnrZb3yKcKijbYxVHVZ4lFonuQDsN3dWonzao'\n",
    "consumer_key = 'nqn05e7YWcj57FBzJhu2vJpQU'\n",
    "consumer_secret = 'a3Y1S2DXVkbRoGaO3NMgstbGm4LSAyRnKSeShseZFrFqWmTwJo'\n",
    "oauth = OAuth(ACCESS_TOKEN, ACCESS_SECRET, consumer_key, consumer_secret)\n",
    "twitter = Twitter(auth=oauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Search for a term\n",
    "search_term = twitter.search.tweets(q = 'Grammys', count = 250)\n",
    "statuses = search_term['statuses']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Normalize the results into a pandas table\n",
    "df2 = json_normalize(search_term, 'statuses')\n",
    "status_texts = df2[\"text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Loop through each row run the sentiment analysis on each sentence\n",
    "polarity = []\n",
    "subjectivity = []\n",
    "for sen in status_texts:\n",
    "    polarity.append(TextBlob(sen).sentiment.polarity)\n",
    "    subjectivity.append(TextBlob(sen).sentiment.subjectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.116667</td>\n",
       "      <td>0.384722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.212500</td>\n",
       "      <td>0.364583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.212500</td>\n",
       "      <td>0.364583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.762963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Polarity  Subjectivity\n",
       "0   0.800000      1.000000\n",
       "1   0.466667      0.762963\n",
       "2   0.000000      0.000000\n",
       "3   0.466667      0.762963\n",
       "4   0.000000      0.000000\n",
       "5   0.466667      0.762963\n",
       "6   0.000000      0.000000\n",
       "7   0.000000      0.000000\n",
       "8   0.000000      0.000000\n",
       "9   0.466667      0.762963\n",
       "10  0.000000      0.000000\n",
       "11  0.466667      0.762963\n",
       "12  0.466667      0.762963\n",
       "13  1.000000      1.000000\n",
       "14  0.733333      0.800000\n",
       "15  0.000000      0.000000\n",
       "16  0.466667      0.762963\n",
       "17  0.470000      0.580000\n",
       "18  0.466667      0.762963\n",
       "19  0.466667      0.762963\n",
       "20  0.300000      0.500000\n",
       "21  0.500000      1.000000\n",
       "22  0.000000      0.000000\n",
       "23  0.733333      0.800000\n",
       "24  0.500000      1.000000\n",
       "25 -0.116667      0.384722\n",
       "26  0.000000      0.000000\n",
       "27  0.466667      0.762963\n",
       "28  0.800000      1.000000\n",
       "29  0.466667      0.762963\n",
       "..       ...           ...\n",
       "70  0.500000      1.000000\n",
       "71  0.212500      0.364583\n",
       "72  0.045455      0.666667\n",
       "73  0.466667      0.762963\n",
       "74  0.466667      0.762963\n",
       "75  0.466667      0.762963\n",
       "76  0.212500      0.364583\n",
       "77 -0.400000      0.600000\n",
       "78  0.466667      0.762963\n",
       "79  0.500000      1.000000\n",
       "80  0.466667      0.762963\n",
       "81  0.466667      0.762963\n",
       "82  0.000000      0.000000\n",
       "83  0.466667      0.762963\n",
       "84  0.466667      0.762963\n",
       "85  0.466667      0.762963\n",
       "86  0.466667      0.762963\n",
       "87  0.466667      0.762963\n",
       "88  1.000000      1.000000\n",
       "89  0.000000      0.000000\n",
       "90  0.466667      0.762963\n",
       "91  0.466667      0.762963\n",
       "92  0.466667      0.762963\n",
       "93 -0.050000      0.400000\n",
       "94  0.466667      0.762963\n",
       "95  0.466667      0.762963\n",
       "96  0.466667      0.762963\n",
       "97  0.466667      0.762963\n",
       "98  0.466667      0.762963\n",
       "99  0.466667      0.762963\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the polarity and subjectivity into another data table\n",
    "df_final = pd.DataFrame({'Polarity' : polarity, 'Subjectivity' : subjectivity})\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2270d44c470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAFkCAYAAABMyWOlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqo/859IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2270d449748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot polarity and subjectivity\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "df_final[['Polarity','Subjectivity']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@teaofstans:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ariana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grande</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-Dates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>every</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>guy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>she</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>collabs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>with</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-Fans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>get</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>brave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>with</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Grammys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>but</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>hasn't</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>won</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-She's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>not</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mariah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Carey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>but</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>her…</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>@WhyLarryIsReal:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Wow.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>will</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>perform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>https://t.co/mWj6jrL4nH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>@WhyLarryIsReal:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>Wow.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>will</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512</th>\n",
       "      <td>perform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514</th>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>grammys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>and</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>for</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1519</th>\n",
       "      <td>sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>https://t.co/mWj6jrL4nH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>RT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>@WhyLarryIsReal:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>Wow.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>will</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>perform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>grammys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>and</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>for</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>https://t.co/mWj6jrL4nH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1535 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "0                          RT\n",
       "1                @teaofstans:\n",
       "2                      Ariana\n",
       "3                      Grande\n",
       "4                      -Dates\n",
       "5                       every\n",
       "6                         guy\n",
       "7                         she\n",
       "8                     collabs\n",
       "9                        with\n",
       "10                      -Fans\n",
       "11                        get\n",
       "12                      brave\n",
       "13                       with\n",
       "14                    Grammys\n",
       "15                        but\n",
       "16                     hasn't\n",
       "17                        won\n",
       "18                     -She's\n",
       "19                        not\n",
       "20                     Mariah\n",
       "21                      Carey\n",
       "22                        but\n",
       "23                       her…\n",
       "24                         RT\n",
       "25           @WhyLarryIsReal:\n",
       "26                       Wow.\n",
       "27                         he\n",
       "28                       will\n",
       "29                    perform\n",
       "...                       ...\n",
       "1505                     sure\n",
       "1506  https://t.co/mWj6jrL4nH\n",
       "1507                       RT\n",
       "1508         @WhyLarryIsReal:\n",
       "1509                     Wow.\n",
       "1510                       he\n",
       "1511                     will\n",
       "1512                  perform\n",
       "1513                       at\n",
       "1514                      the\n",
       "1515                  grammys\n",
       "1516                      and\n",
       "1517                      win\n",
       "1518                      for\n",
       "1519                     sure\n",
       "1520  https://t.co/mWj6jrL4nH\n",
       "1521                       RT\n",
       "1522         @WhyLarryIsReal:\n",
       "1523                     Wow.\n",
       "1524                       he\n",
       "1525                     will\n",
       "1526                  perform\n",
       "1527                       at\n",
       "1528                      the\n",
       "1529                  grammys\n",
       "1530                      and\n",
       "1531                      win\n",
       "1532                      for\n",
       "1533                     sure\n",
       "1534  https://t.co/mWj6jrL4nH\n",
       "\n",
       "[1535 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the tweet messages in the \"text\" column (row by row) and break it into words, store it another data frame\n",
    "status_texts = [status['text'] for status in statuses]\n",
    "words = [w\n",
    "        for t in status_texts\n",
    "            for w in t.split()]\n",
    "df_word = pd.DataFrame(words)\n",
    "df_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>\"Why</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"rocking</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#1s.</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#3YearsTheNewClassic</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#DeleonLifestyle</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#GRAMMYs</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#GrammyNaTNT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#Grammys</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#Grammys!!!!!!!!!!</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#RealHustlasKeepLevelingUp</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#SoleGregorini</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#TBT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#TheNextL…</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#VideoLove</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#grammys</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&amp;amp;</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(CAMILA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(https://t.co/wqPS08lBp2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>)</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-Dates</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-Fans</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-Grammys</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-Hey</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-KCA…</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-Latina,</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-Love</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-She's</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>towards</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twenty</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>un</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>una</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verá</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vid</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>você</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voz</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vídeo</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w/</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watch</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>we're</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>why</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>will</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>with</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>won</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>you</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Álbum</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>é</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>✊🏿✊🏿✊🏿</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>🌹</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>💯</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>😂😂😂</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Count\n",
       "\"Why                            1\n",
       "\"rocking                        1\n",
       "#1                              2\n",
       "#1s.                            1\n",
       "#3YearsTheNewClassic            1\n",
       "#DeleonLifestyle                1\n",
       "#GRAMMYs                        5\n",
       "#GrammyNaTNT                    1\n",
       "#Grammys                        3\n",
       "#Grammys!!!!!!!!!!              2\n",
       "#RealHustlasKeepLevelingUp      1\n",
       "#SoleGregorini                  1\n",
       "#TBT                            1\n",
       "#TheNextL…                      1\n",
       "#VideoLove                      1\n",
       "#grammys                        1\n",
       "&amp;                           4\n",
       "(CAMILA                         1\n",
       "(https://t.co/wqPS08lBp2        1\n",
       ")                               1\n",
       "+                               2\n",
       "-                               2\n",
       "-Dates                          3\n",
       "-Fans                           3\n",
       "-Grammys                        3\n",
       "-Hey                            3\n",
       "-KCA…                           3\n",
       "-Latina,                        3\n",
       "-Love                           3\n",
       "-She's                          3\n",
       "...                           ...\n",
       "total                           1\n",
       "touch                           1\n",
       "towards                         1\n",
       "twenty                          1\n",
       "un                              1\n",
       "una                             2\n",
       "version                         1\n",
       "verá                            1\n",
       "vid                             1\n",
       "você                            1\n",
       "voz                             1\n",
       "vídeo                           1\n",
       "w/                              2\n",
       "was                             3\n",
       "watch                           1\n",
       "we're                           2\n",
       "white                           2\n",
       "why                             1\n",
       "will                           55\n",
       "win                            54\n",
       "with                            6\n",
       "won                             3\n",
       "you                             2\n",
       "yr                              1\n",
       "Álbum                           1\n",
       "é                               1\n",
       "✊🏿✊🏿✊🏿                          4\n",
       "🌹                               2\n",
       "💯                               1\n",
       "😂😂😂                             2\n",
       "\n",
       "[437 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally take the final data frame and group by the frequency (count) of the words\n",
    "words_count = json_normalize(Counter(words))\n",
    "words_count = words_count.transpose()\n",
    "words_count.columns = ['Count']\n",
    "words_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shraddha Somani\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2270d3d8908>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfwAAAH8CAYAAAA0Uh0TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJA2ABV+SpAH4LfuUV0brDcrLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2270d3d8630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph of top 20 words\n",
    "words_top20 = words_count.sort('Count', ascending=False).head(20)\n",
    "words_top20\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "words_top20.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}