Sentiment_Analysis_VADER.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment analysis\n",
    "\n",
    "### Sentiment Analysis, or Opinion Mining, is a sub-field of Natural Language Processing (NLP) that tries to identify and extract opinions within a given text.\n",
    "\n",
    "- sentiment analysis so important because Businesses today are heavily dependent on data. Majority of this data however, is unstructured text coming from sources like emails, chats, social media, surveys, articles, and documents. The micro-blogging content coming from Twitter and Facebook poses serious challenges, not only because of the amount of data involved, but also because of the kind of language used in them to express sentiments, i.e., short forms, memes and emoticons\n",
    "\n",
    "- sentiment analysis is used in all over the world to analyse the people reaction.\n",
    "- Different company use sentiment analysis to analyse the product review.\n",
    "- on the basis of product review company take for decision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tweepy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VADER Sentiment Analysis\n",
    "\n",
    "- VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.\n",
    "\n",
    "- VADER uses a combination of A sentiment lexicon is a list of lexical features (e.g., words) which are generally labelled according to their semantic orientation as either positive or negative.\n",
    "\n",
    "- VADER has been found to be quite successful when dealing with social media texts, NY Times editorials, movie reviews, and product reviews. This is because VADER not only tells about the Positivity and Negativity score but also tells us about how positive or negative a sentiment is.\n",
    "\n",
    "- VADER performs very well with emojis, slangs and acronyms in sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Authenticate keys of twitter\n",
    "consumer_key = 'Enter Here'\n",
    "consumer_secret = 'Enter Here'\n",
    "access_token = 'Enter Here'\n",
    "access_token_secret = 'Enter Here'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @KirkDBorne: Learning Math For #MachineLear...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buen articulo sobre la IA en estos momentos: h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@radiobotics’ machine learning technology, tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Impact of a combination of quantitative indice...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Topological Properties of Resting-State fMRI F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Performance and clinical impact of machine lea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Handmade trileaflet valve design and validatio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prediction of breast cancer risk using a machi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT @IntelBusiness: Improve business processes ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @ElliottSaslow: We are in a digital arms ra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets\n",
       "0  RT @KirkDBorne: Learning Math For #MachineLear...\n",
       "1  Buen articulo sobre la IA en estos momentos: h...\n",
       "2  @radiobotics’ machine learning technology, tha...\n",
       "3  Impact of a combination of quantitative indice...\n",
       "4  Topological Properties of Resting-State fMRI F...\n",
       "5  Performance and clinical impact of machine lea...\n",
       "6  Handmade trileaflet valve design and validatio...\n",
       "7  Prediction of breast cancer risk using a machi...\n",
       "8  RT @IntelBusiness: Improve business processes ...\n",
       "9  RT @ElliottSaslow: We are in a digital arms ra..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-29 13:28:06\n"
     ]
    }
   ],
   "source": [
    "#with the help of key accessing the twitter\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "\n",
    "api = tweepy.API(auth)\n",
    "\n",
    "tweets = api.search('Machine Learning', count=200)\n",
    "\n",
    "\n",
    "data = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
    "\n",
    "display(data.head(10))\n",
    "\n",
    "\n",
    "print(tweets[0].created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading lexicon: Package 'lexicon' not found in\n",
      "[nltk_data]     index\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "# lexicon is considered a corpus since a list of words is also a body of text\n",
    "nltk.download('lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @KirkDBorne: Learning Math For #MachineLear...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buen articulo sobre la IA en estos momentos: h...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@radiobotics’ machine learning technology, tha...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.791, 'pos': 0.209, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Impact of a combination of quantitative indice...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Topological Properties of Resting-State fMRI F...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.805, 'pos': 0.195, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Performance and clinical impact of machine lea...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Handmade trileaflet valve design and validatio...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prediction of breast cancer risk using a machi...</td>\n",
       "      <td>{'neg': 0.317, 'neu': 0.683, 'pos': 0.0, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT @IntelBusiness: Improve business processes ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.573, 'pos': 0.427, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @ElliottSaslow: We are in a digital arms ra...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Increasing Accessibility with Machine Learning...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Increasing Accessibility with Machine Learning...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RT @DD_NaNa_: Getting Value from Machine Learn...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.766, 'pos': 0.234, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Machine learning is a sweet way to tell if you...</td>\n",
       "      <td>{'neg': 0.181, 'neu': 0.643, 'pos': 0.175, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>双曲空間でのMachine Learningの最近の進展 - ABEJA Arts Blog...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Google's Machine Learning Can Help Drivers Avo...</td>\n",
       "      <td>{'neg': 0.283, 'neu': 0.576, 'pos': 0.141, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>I've organized the recent exchange of blog pos...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Getting Value from Machine Learning Isn’t Abou...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.743, 'pos': 0.257, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RT @larsmnagel: I think we are the first Europ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RT @AWS_UKI: This is a great watch! @ShafreenS...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.812, 'pos': 0.188, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RT @hire_olive: Here is our weekly geek out - ...</td>\n",
       "      <td>{'neg': 0.076, 'neu': 0.924, 'pos': 0.0, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>@JoshuaPStarr Feds, states, &amp;amp; districts al...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>\"Christelle Marfaing, Data Scientist chez Lydi...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Looking to introduce AI in your marketing acti...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.913, 'pos': 0.087, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Increasing Accessibility with Machine Learning...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Khan Academy for learning Math https://t.co/BT...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>#IoT, #AI, and machine learning help organizat...</td>\n",
       "      <td>{'neg': 0.166, 'neu': 0.607, 'pos': 0.227, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>RT @DotCSV: El estado de la IA en 2019.\\n\\nhtt...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RT @MusicComposer1: Interesting....The state o...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RT @KirkDBorne: One of my all-time favorites &amp;...</td>\n",
       "      <td>{'neg': 0.103, 'neu': 0.655, 'pos': 0.241, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>RT @willhillpeople: Would you like to work wor...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.636, 'pos': 0.364, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>RT @l2k: I wrote another article: Why are Mach...</td>\n",
       "      <td>{'neg': 0.118, 'neu': 0.882, 'pos': 0.0, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Learning Math For Machine Learning And Artific...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.763, 'pos': 0.237, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>RT @myESR: Explore the fascinating world of AI...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.798, 'pos': 0.202, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>RT @alosefer: #كنز\\nمخزن للبيانات والمعلومات ا...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Would you like to work work with innovative te...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.677, 'pos': 0.323, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>RT @Azure: Reduce the time it takes to build m...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>🧐 Es zeichnet sich ein mögliches Plateau beim ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>RT @KirkDBorne: Learning Math For #MachineLear...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>The state of AI in 2019 https://t.co/17Wu50HWS...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Here is our weekly geek out - learn how to tra...</td>\n",
       "      <td>{'neg': 0.087, 'neu': 0.913, 'pos': 0.0, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Prepare your data management architecture for ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>#MachineLearning Across Industries: Belief, In...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.777, 'pos': 0.223, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>AI, machine learning and big data make @Ciever...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Professor Rob Tibshirani – one of the authors ...</td>\n",
       "      <td>{'neg': 0.155, 'neu': 0.687, 'pos': 0.159, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>RT @DoghouseAI: Increasing accessibility with ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Increasing Accessibility with Machine Learning...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>RT @houdaAjawhar: “It’s better, then, to talk ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.861, 'pos': 0.139, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>So krempeln künstliche Intelligenz &amp;amp; Machi...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Increasing Accessibility with Machine Learning...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Increasing Accessibility with Machine Learning...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>RT @cysecprofs: Big Data, machine learning, da...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.794, 'pos': 0.206, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Humans vs machines: AI and machine learning in...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>RT @DoghouseAI: Increasing accessibility with ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>We can help execute using disparate data and m...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.753, 'pos': 0.247, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>RT @houdaAjawhar: “It’s better, then, to talk ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.861, 'pos': 0.139, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>RT @MindWise_CK: “You’re only as diverse as yo...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.802, 'pos': 0.198, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>RT @KirkDBorne: Learning Math For #MachineLear...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>RT @chchristiaens: This is what’s happening in...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>RT @Inferences_om: Quels sont les langages et ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweets  \\\n",
       "0   RT @KirkDBorne: Learning Math For #MachineLear...   \n",
       "1   Buen articulo sobre la IA en estos momentos: h...   \n",
       "2   @radiobotics’ machine learning technology, tha...   \n",
       "3   Impact of a combination of quantitative indice...   \n",
       "4   Topological Properties of Resting-State fMRI F...   \n",
       "5   Performance and clinical impact of machine lea...   \n",
       "6   Handmade trileaflet valve design and validatio...   \n",
       "7   Prediction of breast cancer risk using a machi...   \n",
       "8   RT @IntelBusiness: Improve business processes ...   \n",
       "9   RT @ElliottSaslow: We are in a digital arms ra...   \n",
       "10  Increasing Accessibility with Machine Learning...   \n",
       "11  Increasing Accessibility with Machine Learning...   \n",
       "12  RT @DD_NaNa_: Getting Value from Machine Learn...   \n",
       "13  Machine learning is a sweet way to tell if you...   \n",
       "14  双曲空間でのMachine Learningの最近の進展 - ABEJA Arts Blog...   \n",
       "15  Google's Machine Learning Can Help Drivers Avo...   \n",
       "16  I've organized the recent exchange of blog pos...   \n",
       "17  Getting Value from Machine Learning Isn’t Abou...   \n",
       "18  RT @larsmnagel: I think we are the first Europ...   \n",
       "19  RT @AWS_UKI: This is a great watch! @ShafreenS...   \n",
       "20  RT @hire_olive: Here is our weekly geek out - ...   \n",
       "21  @JoshuaPStarr Feds, states, &amp; districts al...   \n",
       "22  \"Christelle Marfaing, Data Scientist chez Lydi...   \n",
       "23  Looking to introduce AI in your marketing acti...   \n",
       "24  Increasing Accessibility with Machine Learning...   \n",
       "25  Khan Academy for learning Math https://t.co/BT...   \n",
       "26  #IoT, #AI, and machine learning help organizat...   \n",
       "27  RT @DotCSV: El estado de la IA en 2019.\\n\\nhtt...   \n",
       "28  RT @MusicComposer1: Interesting....The state o...   \n",
       "29  RT @KirkDBorne: One of my all-time favorites &...   \n",
       "..                                                ...   \n",
       "70  RT @willhillpeople: Would you like to work wor...   \n",
       "71  RT @l2k: I wrote another article: Why are Mach...   \n",
       "72  Learning Math For Machine Learning And Artific...   \n",
       "73  RT @myESR: Explore the fascinating world of AI...   \n",
       "74  RT @alosefer: #كنز\\nمخزن للبيانات والمعلومات ا...   \n",
       "75  Would you like to work work with innovative te...   \n",
       "76  RT @Azure: Reduce the time it takes to build m...   \n",
       "77  🧐 Es zeichnet sich ein mögliches Plateau beim ...   \n",
       "78  RT @KirkDBorne: Learning Math For #MachineLear...   \n",
       "79  The state of AI in 2019 https://t.co/17Wu50HWS...   \n",
       "80  Here is our weekly geek out - learn how to tra...   \n",
       "81  Prepare your data management architecture for ...   \n",
       "82  #MachineLearning Across Industries: Belief, In...   \n",
       "83  AI, machine learning and big data make @Ciever...   \n",
       "84  Professor Rob Tibshirani – one of the authors ...   \n",
       "85  RT @DoghouseAI: Increasing accessibility with ...   \n",
       "86  Increasing Accessibility with Machine Learning...   \n",
       "87  RT @houdaAjawhar: “It’s better, then, to talk ...   \n",
       "88  So krempeln künstliche Intelligenz &amp; Machi...   \n",
       "89  Increasing Accessibility with Machine Learning...   \n",
       "90  Increasing Accessibility with Machine Learning...   \n",
       "91  RT @cysecprofs: Big Data, machine learning, da...   \n",
       "92  Humans vs machines: AI and machine learning in...   \n",
       "93  RT @DoghouseAI: Increasing accessibility with ...   \n",
       "94  We can help execute using disparate data and m...   \n",
       "95  RT @houdaAjawhar: “It’s better, then, to talk ...   \n",
       "96  RT @MindWise_CK: “You’re only as diverse as yo...   \n",
       "97  RT @KirkDBorne: Learning Math For #MachineLear...   \n",
       "98  RT @chchristiaens: This is what’s happening in...   \n",
       "99  RT @Inferences_om: Quels sont les langages et ...   \n",
       "\n",
       "                                             polarity  \n",
       "0   {'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'comp...  \n",
       "1   {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "2   {'neg': 0.0, 'neu': 0.791, 'pos': 0.209, 'comp...  \n",
       "3   {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "4   {'neg': 0.0, 'neu': 0.805, 'pos': 0.195, 'comp...  \n",
       "5   {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "6   {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "7   {'neg': 0.317, 'neu': 0.683, 'pos': 0.0, 'comp...  \n",
       "8   {'neg': 0.0, 'neu': 0.573, 'pos': 0.427, 'comp...  \n",
       "9   {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "10  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "11  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "12  {'neg': 0.0, 'neu': 0.766, 'pos': 0.234, 'comp...  \n",
       "13  {'neg': 0.181, 'neu': 0.643, 'pos': 0.175, 'co...  \n",
       "14  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "15  {'neg': 0.283, 'neu': 0.576, 'pos': 0.141, 'co...  \n",
       "16  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "17  {'neg': 0.0, 'neu': 0.743, 'pos': 0.257, 'comp...  \n",
       "18  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "19  {'neg': 0.0, 'neu': 0.812, 'pos': 0.188, 'comp...  \n",
       "20  {'neg': 0.076, 'neu': 0.924, 'pos': 0.0, 'comp...  \n",
       "21  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "22  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "23  {'neg': 0.0, 'neu': 0.913, 'pos': 0.087, 'comp...  \n",
       "24  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "25  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "26  {'neg': 0.166, 'neu': 0.607, 'pos': 0.227, 'co...  \n",
       "27  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "28  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "29  {'neg': 0.103, 'neu': 0.655, 'pos': 0.241, 'co...  \n",
       "..                                                ...  \n",
       "70  {'neg': 0.0, 'neu': 0.636, 'pos': 0.364, 'comp...  \n",
       "71  {'neg': 0.118, 'neu': 0.882, 'pos': 0.0, 'comp...  \n",
       "72  {'neg': 0.0, 'neu': 0.763, 'pos': 0.237, 'comp...  \n",
       "73  {'neg': 0.0, 'neu': 0.798, 'pos': 0.202, 'comp...  \n",
       "74  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "75  {'neg': 0.0, 'neu': 0.677, 'pos': 0.323, 'comp...  \n",
       "76  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "77  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "78  {'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'comp...  \n",
       "79  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "80  {'neg': 0.087, 'neu': 0.913, 'pos': 0.0, 'comp...  \n",
       "81  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "82  {'neg': 0.0, 'neu': 0.777, 'pos': 0.223, 'comp...  \n",
       "83  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "84  {'neg': 0.155, 'neu': 0.687, 'pos': 0.159, 'co...  \n",
       "85  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "86  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "87  {'neg': 0.0, 'neu': 0.861, 'pos': 0.139, 'comp...  \n",
       "88  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "89  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "90  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "91  {'neg': 0.0, 'neu': 0.794, 'pos': 0.206, 'comp...  \n",
       "92  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "93  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "94  {'neg': 0.0, 'neu': 0.753, 'pos': 0.247, 'comp...  \n",
       "95  {'neg': 0.0, 'neu': 0.861, 'pos': 0.139, 'comp...  \n",
       "96  {'neg': 0.0, 'neu': 0.802, 'pos': 0.198, 'comp...  \n",
       "97  {'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'comp...  \n",
       "98  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "99  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating the object for analysing the polarity\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "\n",
    "listy = []\n",
    "\n",
    "for index, row in data.iterrows():\n",
    "  ss = sid.polarity_scores(row[\"Tweets\"])\n",
    "  listy.append(ss)\n",
    "  \n",
    "se = pd.Series(listy)\n",
    "data['polarity'] = se.values\n",
    "\n",
    "display(data.head(100))"
   ]
  },
  {
   "attachments": {
    "1_G8yV2iaqqfaGfmRPRem2Fw.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Compound score is a metric that calculates the sum of all the lexicon ratings which have been normalized between -1(most extreme negative) and +1 (most extreme positive).\n",
    "\n",
    "![1_G8yV2iaqqfaGfmRPRem2Fw.png](attachment:1_G8yV2iaqqfaGfmRPRem2Fw.png)"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}