TwitterData_Blog.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import json\n",
    "import pandas as pd\n",
    "from scipy.misc import imread\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import matplotlib as mpl\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import operator\n",
    "from textblob import TextBlob\n",
    "from textblob import Word\n",
    "from textblob.sentiments import NaiveBayesAnalyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Authentication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "consumer_key = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'\n",
    "consumer_secret = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'\n",
    "access_token = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxx'\n",
    "access_token_secret = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret) #Interacting with twitter's API\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API (auth) #creating the API object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting Tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "2000\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for tweet in tweepy.Cursor (api.search, q = 'millennials', lang = \"en\").items(2000): \n",
    "    results.append(tweet)\n",
    "    \n",
    "print (type(results))\n",
    "print (len(results))\n",
    "#print (results[4000].text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Store Data in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tweets_df(results):\n",
    "    id_list = [tweet.id for tweet  in results]\n",
    "    data_set = pd.DataFrame(id_list, columns = [\"id\"])\n",
    "    \n",
    "    data_set[\"text\"] = [tweet.text for tweet in results]\n",
    "    data_set[\"created_at\"] = [tweet.created_at for tweet in results]\n",
    "    data_set[\"retweet_count\"] = [tweet.retweet_count for tweet in results]\n",
    "    data_set[\"user_screen_name\"] = [tweet.author.screen_name for tweet in results]\n",
    "    data_set[\"user_followers_count\"] = [tweet.author.followers_count for tweet in results]\n",
    "    data_set[\"user_location\"] = [tweet.author.location for tweet in results]\n",
    "    data_set[\"Hashtags\"] = [tweet.entities.get('hashtags') for tweet in results]\n",
    "    \n",
    "    return data_set\n",
    "data_set = tweets_df(results)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove duplicate tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = data_set[\"text\"]\n",
    "\n",
    "for i in range(0,len(text)):\n",
    "    txt = ' '.join(word for word in text[i] .split() if not word.startswith('https:'))\n",
    "    data_set.set_value(i, 'text2', txt)\n",
    "    \n",
    "data_set.drop_duplicates('text2', inplace=True)\n",
    "data_set.reset_index(drop = True, inplace=True)\n",
    "data_set.drop('text', axis = 1, inplace = True)\n",
    "data_set.rename(columns={'text2': 'text'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis of tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = data_set[\"text\"]\n",
    "\n",
    "for i in range(0,len(text)):\n",
    "    textB = TextBlob(text[i])\n",
    "    sentiment = textB.sentiment.polarity\n",
    "    data_set.set_value(i, 'Sentiment',sentiment)\n",
    "    if sentiment <0.00:\n",
    "        SentimentClass = 'Negative'\n",
    "        data_set.set_value(i, 'SentimentClass', SentimentClass )\n",
    "    elif sentiment >0.00:\n",
    "        SentimentClass = 'Positive'\n",
    "        data_set.set_value(i, 'SentimentClass', SentimentClass )\n",
    "    else:\n",
    "        SentimentClass = 'Neutral'\n",
    "        data_set.set_value(i, 'SentimentClass', SentimentClass )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set.to_csv(\"C:\\\\Users\\\\kdudani\\\\Desktop\\\\Personal\\\\Social Media Analytics\\\\Millennials.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract all hashtags for all tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Htag_df = pd.DataFrame()\n",
    "j = 0\n",
    "\n",
    "for tweet in range(0,len(results)):\n",
    "    hashtag = results[tweet].entities.get('hashtags')\n",
    "    for i in range(0,len(hashtag)):\n",
    "        Htag = hashtag[i]['text'] \n",
    "        Htag_df.set_value(j, 'Hashtag',Htag)\n",
    "        j = j+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Hashtag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>venture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>talent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>manufacturers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mobility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fintech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>finserv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>banking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>babyboomers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>babyboomers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GenZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SupplyChain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>VR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>IoT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Wearables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>OutNumbered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ConceptCar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>LaborDay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>fastfood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>Podcast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>GG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>GenX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>Teen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>nextgen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>entrepreneur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>nextgen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>kbye</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>digitalPR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>Polls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>Levo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>millennials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>housing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>GenZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557</th>\n",
       "      <td>Millennials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1558 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Hashtag\n",
       "0                vc\n",
       "1           venture\n",
       "2            talent\n",
       "3     manufacturers\n",
       "4          mobility\n",
       "5           fintech\n",
       "6           finserv\n",
       "7           banking\n",
       "8                in\n",
       "9           College\n",
       "10      Millennials\n",
       "11      Millennials\n",
       "12      babyboomers\n",
       "13      Millennials\n",
       "14      babyboomers\n",
       "15      Millennials\n",
       "16             GenZ\n",
       "17      SupplyChain\n",
       "18               AR\n",
       "19               VR\n",
       "20              IoT\n",
       "21        Wearables\n",
       "22      Millennials\n",
       "23               AI\n",
       "24               2A\n",
       "25        marketing\n",
       "26        marketing\n",
       "27              fox\n",
       "28      OutNumbered\n",
       "29       ConceptCar\n",
       "...             ...\n",
       "1528       LaborDay\n",
       "1529           food\n",
       "1530       fastfood\n",
       "1531    Millennials\n",
       "1532        Podcast\n",
       "1533             GG\n",
       "1534        Boomers\n",
       "1535           GenX\n",
       "1536    Millennials\n",
       "1537           Teen\n",
       "1538        College\n",
       "1539        nextgen\n",
       "1540    millennials\n",
       "1541   entrepreneur\n",
       "1542        nextgen\n",
       "1543    millennials\n",
       "1544           kbye\n",
       "1545      digitalPR\n",
       "1546          Polls\n",
       "1547    Millennials\n",
       "1548           meat\n",
       "1549    millennials\n",
       "1550           Levo\n",
       "1551    Millennials\n",
       "1552           meat\n",
       "1553    millennials\n",
       "1554           food\n",
       "1555        housing\n",
       "1556           GenZ\n",
       "1557    Millennials\n",
       "\n",
       "[1558 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Htag_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Millennials_Htag_wordcloud = Htag_df.groupby('Hashtag').size()\n",
    "Millennials_Htag_wordcloud.to_csv(\"C:\\\\Users\\\\kdudani\\\\Desktop\\\\Personal\\\\Social Media Analytics\\\\Millennials_Htag_wordcloud.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Continuum\\Anaconda3\\lib\\site-packages\\wordcloud\\wordcloud.py:372: UserWarning: mask image should be unsigned byte between 0 and 255. Got a float array\n",
      "  warnings.warn(\"mask image should be unsigned byte between 0\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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aXYel+2q5LvagWnszLeePQ5NrcXJw5QrvQjYpzXMIMyMiorchG+nKz/Zy6S8D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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d5505622b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Join all the text from the 1000 tweets\n",
    "Hashtag_Combined = \" \".join(Htag_df['Hashtag'].values.astype(str))\n",
    "\n",
    "no_millennials = \" \".join([word for word in Hashtag_Combined.split()\n",
    "                                if word != 'millennials'\n",
    "                                and word != 'Millennials'\n",
    "                                and word != 'Boomers'\n",
    "                                and word != 'GenX'\n",
    "                                                                \n",
    "                                ])\n",
    "\n",
    "Tweet_mask = imread(\"C:\\\\Users\\\\kdudani\\\\Desktop\\\\Personal\\\\Social Media Analytics\\\\twitter_mask.png\", flatten=True)\n",
    "\n",
    "#Create a Word Cloud\n",
    "wc = WordCloud(background_color=\"white\", stopwords=STOPWORDS, mask = Tweet_mask)\n",
    "wc.generate(no_millennials)\n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('C:\\\\Users\\\\kdudani\\\\Desktop\\\\Personal\\\\Social Media Analytics\\\\millennials_Hashtag.png', dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "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
}