Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy           # To consume Twitter's API\n",
    "import pandas as pd     # To handle data\n",
    "import numpy as np      # For number computing\n",
    "\n",
    "# For plotting and visualization:\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACCESS_TOKEN = \"1094926398-xjds9JDzgcoKpov7AqZQB1OSOa83P6Ot1ISf5PQ\"\n",
    "ACCESS_SECRET =\"EdMgr9HC6IIXYgwUp2VDxfrEtgfdbiLe1ziA7HeChoIsG\" \n",
    "CONSUMER_KEY =\"UaPhFj3tRVHEzrKkjkIT32HFb\" \n",
    "CONSUMER_SECRET =\"UrUCOkdmPrdNhdG4inCBZvhfDUoHZOD7x0VL9HWzCBPAcpFIaM\"     # This will allow us to use the keys as variables\n",
    "\n",
    "# API's setup:\n",
    "def twitter_setup():\n",
    "   \n",
    "    # Authentication and access using keys:\n",
    "    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n",
    "    auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n",
    " \n",
    "\n",
    "    \n",
    "    # Return API with authentication:\n",
    "    api = tweepy.API(auth)\n",
    "    return api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tweets extracted: 200.\n",
      "\n",
      "10 recent tweets:\n",
      "\n",
      "Congratulations to Senator Cindy Hyde-Smith on your big WIN in the Great State of Mississippi. We are all very proud of you!\n",
      "\n",
      "Brenda Snipes, in charge of voting in Broward County, Florida, was just spotted wearing a beautiful dress with 300… https://t.co/gPIS4u4r7Q\n",
      "\n",
      "The Mueller Witch Hunt is a total disgrace. They are looking at supposedly stolen Crooked Hillary Clinton Emails (e… https://t.co/Hd4mV8lxv2\n",
      "\n",
      "....for electric cars. General Motors made a big China bet years ago when they built plants there (and in Mexico) -… https://t.co/0U7lwZ3pfy\n",
      "\n",
      "Very disappointed with General Motors and their CEO, Mary Barra, for closing plants in Ohio, Michigan and Maryland.… https://t.co/5JUbkq1Xkg\n",
      "\n",
      "Polls are open in Mississippi. We need Cindy Hyde-Smith in Washington. GO OUT AND VOTE. Thanks!\n",
      "\n",
      "....terrible Gang of Angry Democrats. Look at their past, and look where they come from. The now $30,000,000 Witch… https://t.co/g3rup9iqga\n",
      "\n",
      "....The Fake News Media builds Bob Mueller up as a Saint, when in actuality he is the exact opposite. He is doing T… https://t.co/kpN479HQns\n",
      "\n",
      "The Phony Witch Hunt continues, but Mueller and his gang of Angry Dems are only looking at one side, not the other.… https://t.co/zfYQKl1a2I\n",
      "\n",
      "RT @GOP: “Tomorrow, the voters of this state will cast their ballots in one of the most important Senate elections of your lives—of all of…\n",
      "\n"
     ]
    }
   ],
   "source": [
    "extractor = twitter_setup()\n",
    "\n",
    "# We create a tweet list as follows:\n",
    "\n",
    "tweets = extractor.user_timeline(screen_name=\"realDonaldTrump\", count=500)\n",
    "print(\"Number of tweets extracted: {}.\\n\".format(len(tweets)))\n",
    "\n",
    "# We print the most recent 5 tweets:\n",
    "print(\"10 recent tweets:\\n\")\n",
    "for tweet in tweets[:10]:\n",
    "    \n",
    "    print(tweet.text)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Congratulations to Senator Cindy Hyde-Smith on...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brenda Snipes, in charge of voting in Broward ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Mueller Witch Hunt is a total disgrace. Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>....for electric cars. General Motors made a b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Very disappointed with General Motors and thei...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Polls are open in Mississippi. We need Cindy H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>....terrible Gang of Angry Democrats. Look at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>....The Fake News Media builds Bob Mueller up ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Phony Witch Hunt continues, but Mueller an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @GOP: “Tomorrow, the voters of this state w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets\n",
       "0  Congratulations to Senator Cindy Hyde-Smith on...\n",
       "1  Brenda Snipes, in charge of voting in Broward ...\n",
       "2  The Mueller Witch Hunt is a total disgrace. Th...\n",
       "3  ....for electric cars. General Motors made a b...\n",
       "4  Very disappointed with General Motors and thei...\n",
       "5  Polls are open in Mississippi. We need Cindy H...\n",
       "6  ....terrible Gang of Angry Democrats. Look at ...\n",
       "7  ....The Fake News Media builds Bob Mueller up ...\n",
       "8  The Phony Witch Hunt continues, but Mueller an...\n",
       "9  RT @GOP: “Tomorrow, the voters of this state w..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
    "\n",
    "# We display the first 10 elements of the dataframe:\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_api', '_json', 'author', 'contributors', 'coordinates', 'created_at', 'destroy', 'entities', 'favorite', 'favorite_count', 'favorited', 'geo', 'id', 'id_str', 'in_reply_to_screen_name', 'in_reply_to_status_id', 'in_reply_to_status_id_str', 'in_reply_to_user_id', 'in_reply_to_user_id_str', 'is_quote_status', 'lang', 'parse', 'parse_list', 'place', 'retweet', 'retweet_count', 'retweeted', 'retweets', 'source', 'source_url', 'text', 'truncated', 'user']\n"
     ]
    }
   ],
   "source": [
    "# Internal methods of a single tweet object:\n",
    "print(dir(tweets[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1067624656943935493\n",
      "2018-11-28 03:42:08\n",
      "Twitter for iPhone\n",
      "43094\n",
      "10419\n",
      "None\n",
      "None\n",
      "{'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': []}\n"
     ]
    }
   ],
   "source": [
    "print(tweets[0].id)\n",
    "print(tweets[0].created_at)\n",
    "print(tweets[0].source)\n",
    "print(tweets[0].favorite_count)\n",
    "print(tweets[0].retweet_count)\n",
    "print(tweets[0].geo)\n",
    "print(tweets[0].coordinates)\n",
    "print(tweets[0].entities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['len']  = np.array([len(tweet.text) for tweet in tweets])\n",
    "data['ID']   = np.array([tweet.id for tweet in tweets])\n",
    "data['Date'] = np.array([tweet.created_at for tweet in tweets])\n",
    "data['Source'] = np.array([tweet.source for tweet in tweets])\n",
    "data['Likes']  = np.array([tweet.favorite_count for tweet in tweets])\n",
    "data['RTs']    = np.array([tweet.retweet_count for tweet in tweets])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\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>len</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Source</th>\n",
       "      <th>Likes</th>\n",
       "      <th>RTs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Congratulations to Senator Cindy Hyde-Smith on...</td>\n",
       "      <td>124</td>\n",
       "      <td>1067624656943935493</td>\n",
       "      <td>2018-11-28 03:42:08</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>43094</td>\n",
       "      <td>10419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brenda Snipes, in charge of voting in Broward ...</td>\n",
       "      <td>139</td>\n",
       "      <td>1067623816921272325</td>\n",
       "      <td>2018-11-28 03:38:48</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>39829</td>\n",
       "      <td>9674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Mueller Witch Hunt is a total disgrace. Th...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067576713859817472</td>\n",
       "      <td>2018-11-28 00:31:37</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>69488</td>\n",
       "      <td>20472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>....for electric cars. General Motors made a b...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067494682249318402</td>\n",
       "      <td>2018-11-27 19:05:39</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>85291</td>\n",
       "      <td>20009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Very disappointed with General Motors and thei...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067494680416407552</td>\n",
       "      <td>2018-11-27 19:05:39</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>96053</td>\n",
       "      <td>24753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Polls are open in Mississippi. We need Cindy H...</td>\n",
       "      <td>95</td>\n",
       "      <td>1067410952042283010</td>\n",
       "      <td>2018-11-27 13:32:57</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>61826</td>\n",
       "      <td>16537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>....terrible Gang of Angry Democrats. Look at ...</td>\n",
       "      <td>139</td>\n",
       "      <td>1067404517841518593</td>\n",
       "      <td>2018-11-27 13:07:23</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>88930</td>\n",
       "      <td>24921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>....The Fake News Media builds Bob Mueller up ...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067398375337791488</td>\n",
       "      <td>2018-11-27 12:42:58</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>81293</td>\n",
       "      <td>22235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Phony Witch Hunt continues, but Mueller an...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067395266511347713</td>\n",
       "      <td>2018-11-27 12:30:37</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>80633</td>\n",
       "      <td>22939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @GOP: “Tomorrow, the voters of this state w...</td>\n",
       "      <td>139</td>\n",
       "      <td>1067280598128095232</td>\n",
       "      <td>2018-11-27 04:54:58</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>8727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets  len  \\\n",
       "0  Congratulations to Senator Cindy Hyde-Smith on...  124   \n",
       "1  Brenda Snipes, in charge of voting in Broward ...  139   \n",
       "2  The Mueller Witch Hunt is a total disgrace. Th...  140   \n",
       "3  ....for electric cars. General Motors made a b...  140   \n",
       "4  Very disappointed with General Motors and thei...  140   \n",
       "5  Polls are open in Mississippi. We need Cindy H...   95   \n",
       "6  ....terrible Gang of Angry Democrats. Look at ...  139   \n",
       "7  ....The Fake News Media builds Bob Mueller up ...  140   \n",
       "8  The Phony Witch Hunt continues, but Mueller an...  140   \n",
       "9  RT @GOP: “Tomorrow, the voters of this state w...  139   \n",
       "\n",
       "                    ID                Date              Source  Likes    RTs  \n",
       "0  1067624656943935493 2018-11-28 03:42:08  Twitter for iPhone  43094  10419  \n",
       "1  1067623816921272325 2018-11-28 03:38:48  Twitter for iPhone  39829   9674  \n",
       "2  1067576713859817472 2018-11-28 00:31:37  Twitter for iPhone  69488  20472  \n",
       "3  1067494682249318402 2018-11-27 19:05:39  Twitter for iPhone  85291  20009  \n",
       "4  1067494680416407552 2018-11-27 19:05:39  Twitter for iPhone  96053  24753  \n",
       "5  1067410952042283010 2018-11-27 13:32:57  Twitter for iPhone  61826  16537  \n",
       "6  1067404517841518593 2018-11-27 13:07:23  Twitter for iPhone  88930  24921  \n",
       "7  1067398375337791488 2018-11-27 12:42:58  Twitter for iPhone  81293  22235  \n",
       "8  1067395266511347713 2018-11-27 12:30:37  Twitter for iPhone  80633  22939  \n",
       "9  1067280598128095232 2018-11-27 04:54:58  Twitter for iPhone      0   8727  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lenght's average in tweets: 128.79\n"
     ]
    }
   ],
   "source": [
    "mean = np.mean(data['len'])\n",
    "\n",
    "print(\"The lenght's average in tweets: {}\".format(mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tweet with more likes is: \n",
      "Tremendous success tonight. Thank you to all!\n",
      "Number of likes: 253245\n",
      "45 characters.\n",
      "\n",
      "The tweet with more retweets is: \n",
      "RT @realDonaldTrump: The Fake News is showing old footage of people climbing over our Ocean Area Fence. This is what it really looks like -…\n",
      "Number of retweets: 51812\n",
      "140 characters.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fav_max = np.max(data['Likes'])\n",
    "rt_max  = np.max(data['RTs'])\n",
    "\n",
    "fav = data[data.Likes == fav_max].index[0]\n",
    "rt  = data[data.RTs == rt_max].index[0]\n",
    "\n",
    "# Max FAVs:\n",
    "print(\"The tweet with more likes is: \\n{}\".format(data['Tweets'][fav]))\n",
    "print(\"Number of likes: {}\".format(fav_max))\n",
    "print(\"{} characters.\\n\".format(data['len'][fav]))\n",
    "\n",
    "# Max RTs:\n",
    "print(\"The tweet with more retweets is: \\n{}\".format(data['Tweets'][rt]))\n",
    "print(\"Number of retweets: {}\".format(rt_max))\n",
    "print(\"{} characters.\\n\".format(data['len'][rt]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tlen = pd.Series(data=data['len'].values, index=data['Date'])\n",
    "tfav = pd.Series(data=data['Likes'].values, index=data['Date'])\n",
    "tret = pd.Series(data=data['RTs'].values, index=data['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e78aebbac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tlen.plot(figsize=(16,4), color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e78b008ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfav.plot(figsize=(16,4), label=\"Likes\", legend=True)\n",
    "tret.plot(figsize=(16,4), label=\"Retweets\", legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creation of content sources:\n",
      "* Twitter for iPhone\n",
      "* Media Studio\n"
     ]
    }
   ],
   "source": [
    "sources = []\n",
    "for source in data['Source']:\n",
    "    if source not in sources:\n",
    "        sources.append(source)\n",
    "\n",
    "# We print sources list:\n",
    "print(\"Creation of content sources:\")\n",
    "for source in sources:\n",
    "    print(\"* {}\".format(source))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e78b145860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent = np.zeros(len(sources))\n",
    "\n",
    "for source in data['Source']:\n",
    "    for index in range(len(sources)):\n",
    "        if source == sources[index]:\n",
    "            percent[index] += 1\n",
    "            pass\n",
    "\n",
    "percent /= 100\n",
    "\n",
    "# Pie chart:\n",
    "pie_chart = pd.Series(percent, index=sources, name='Sources')\n",
    "pie_chart.plot.pie(fontsize=11, autopct='%.2f', figsize=(6, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "import re\n",
    "\n",
    "def clean_tweet(tweet):\n",
    "    '''\n",
    "    Utility function to clean the text in a tweet by removing \n",
    "    links and special characters using regex.\n",
    "    '''\n",
    "    return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())\n",
    "\n",
    "def analize_sentiment(tweet):\n",
    "    '''\n",
    "    Utility function to classify the polarity of a tweet\n",
    "    using textblob.\n",
    "    '''\n",
    "    analysis = TextBlob(clean_tweet(tweet))\n",
    "    if analysis.sentiment.polarity > 0:\n",
    "        return 1\n",
    "    elif analysis.sentiment.polarity == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
       "      <th>len</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Source</th>\n",
       "      <th>Likes</th>\n",
       "      <th>RTs</th>\n",
       "      <th>SA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Congratulations to Senator Cindy Hyde-Smith on...</td>\n",
       "      <td>124</td>\n",
       "      <td>1067624656943935493</td>\n",
       "      <td>2018-11-28 03:42:08</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>43094</td>\n",
       "      <td>10419</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brenda Snipes, in charge of voting in Broward ...</td>\n",
       "      <td>139</td>\n",
       "      <td>1067623816921272325</td>\n",
       "      <td>2018-11-28 03:38:48</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>39829</td>\n",
       "      <td>9674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Mueller Witch Hunt is a total disgrace. Th...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067576713859817472</td>\n",
       "      <td>2018-11-28 00:31:37</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>69488</td>\n",
       "      <td>20472</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>....for electric cars. General Motors made a b...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067494682249318402</td>\n",
       "      <td>2018-11-27 19:05:39</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>85291</td>\n",
       "      <td>20009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Very disappointed with General Motors and thei...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067494680416407552</td>\n",
       "      <td>2018-11-27 19:05:39</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>96053</td>\n",
       "      <td>24753</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Polls are open in Mississippi. We need Cindy H...</td>\n",
       "      <td>95</td>\n",
       "      <td>1067410952042283010</td>\n",
       "      <td>2018-11-27 13:32:57</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>61826</td>\n",
       "      <td>16537</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>....terrible Gang of Angry Democrats. Look at ...</td>\n",
       "      <td>139</td>\n",
       "      <td>1067404517841518593</td>\n",
       "      <td>2018-11-27 13:07:23</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>88930</td>\n",
       "      <td>24921</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>....The Fake News Media builds Bob Mueller up ...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067398375337791488</td>\n",
       "      <td>2018-11-27 12:42:58</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>81293</td>\n",
       "      <td>22235</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Phony Witch Hunt continues, but Mueller an...</td>\n",
       "      <td>140</td>\n",
       "      <td>1067395266511347713</td>\n",
       "      <td>2018-11-27 12:30:37</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>80633</td>\n",
       "      <td>22939</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @GOP: “Tomorrow, the voters of this state w...</td>\n",
       "      <td>139</td>\n",
       "      <td>1067280598128095232</td>\n",
       "      <td>2018-11-27 04:54:58</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>8727</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets  len  \\\n",
       "0  Congratulations to Senator Cindy Hyde-Smith on...  124   \n",
       "1  Brenda Snipes, in charge of voting in Broward ...  139   \n",
       "2  The Mueller Witch Hunt is a total disgrace. Th...  140   \n",
       "3  ....for electric cars. General Motors made a b...  140   \n",
       "4  Very disappointed with General Motors and thei...  140   \n",
       "5  Polls are open in Mississippi. We need Cindy H...   95   \n",
       "6  ....terrible Gang of Angry Democrats. Look at ...  139   \n",
       "7  ....The Fake News Media builds Bob Mueller up ...  140   \n",
       "8  The Phony Witch Hunt continues, but Mueller an...  140   \n",
       "9  RT @GOP: “Tomorrow, the voters of this state w...  139   \n",
       "\n",
       "                    ID                Date              Source  Likes    RTs  \\\n",
       "0  1067624656943935493 2018-11-28 03:42:08  Twitter for iPhone  43094  10419   \n",
       "1  1067623816921272325 2018-11-28 03:38:48  Twitter for iPhone  39829   9674   \n",
       "2  1067576713859817472 2018-11-28 00:31:37  Twitter for iPhone  69488  20472   \n",
       "3  1067494682249318402 2018-11-27 19:05:39  Twitter for iPhone  85291  20009   \n",
       "4  1067494680416407552 2018-11-27 19:05:39  Twitter for iPhone  96053  24753   \n",
       "5  1067410952042283010 2018-11-27 13:32:57  Twitter for iPhone  61826  16537   \n",
       "6  1067404517841518593 2018-11-27 13:07:23  Twitter for iPhone  88930  24921   \n",
       "7  1067398375337791488 2018-11-27 12:42:58  Twitter for iPhone  81293  22235   \n",
       "8  1067395266511347713 2018-11-27 12:30:37  Twitter for iPhone  80633  22939   \n",
       "9  1067280598128095232 2018-11-27 04:54:58  Twitter for iPhone      0   8727   \n",
       "\n",
       "   SA  \n",
       "0   1  \n",
       "1   1  \n",
       "2   0  \n",
       "3   1  \n",
       "4  -1  \n",
       "5   1  \n",
       "6  -1  \n",
       "7  -1  \n",
       "8  -1  \n",
       "9   1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['SA'] = np.array([ analize_sentiment(tweet) for tweet in data['Tweets'] ])\n",
    "\n",
    "# We display the updated dataframe with the new column:\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tweets = [ tweet for index, tweet in enumerate(data['Tweets']) if data['SA'][index] > 0]\n",
    "neu_tweets = [ tweet for index, tweet in enumerate(data['Tweets']) if data['SA'][index] == 0]\n",
    "neg_tweets = [ tweet for index, tweet in enumerate(data['Tweets']) if data['SA'][index] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of positive tweets: 57.5%\n",
      "Percentage of neutral tweets: 25.0%\n",
      "Percentage de negative tweets: 17.5%\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of positive tweets: {}%\".format(len(pos_tweets)*100/len(data['Tweets'])))\n",
    "print(\"Percentage of neutral tweets: {}%\".format(len(neu_tweets)*100/len(data['Tweets'])))\n",
    "print(\"Percentage de negative tweets: {}%\".format(len(neg_tweets)*100/len(data['Tweets'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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