UNICRI-Decode-CW/twitter-sentiment-analysis-Single-User.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Sentiment analysis using the public twitter API access:\n",
    "\n",
    "For the purpose of this event, feel free to use API keys below.\n",
    "To be able to do more extensive research, you need to apply for the Twitter \"developer\" account.\n",
    "See this article for more details on how to apply:\n",
    "https://www.codementor.io/@ferrorodolfo/sentiment-analysis-on-trump-s-tweets-using-python-pltbvb4xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, load the API credentials:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is also saved in file credentials.py\n",
    "# Twitter App access keys for @user\n",
    "#https://developer.twitter.com/en/apps\n",
    "\n",
    "# Consume:\n",
    "CONSUMER_KEY    = 'qg907KH5lNPeXtGHIE0RLClKt'\n",
    "CONSUMER_SECRET = 'sQTi3MIOhDCuGhnlttyFG9ynVjEHhnIvnJ3Y3KC6QPuBfTAXaf'\n",
    "\n",
    "# Access:\n",
    "ACCESS_TOKEN  = '1925775572-BwFQZLGkFl1r4ViYoAi7ibnT9sCW8lUHE7wC60O'\n",
    "ACCESS_SECRET = 'FOc5cFqgk3AEZtiRSfnfhVsJdLJbbuHSlrK2WJncTusjI'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next load the code setup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# API's setup:\n",
    "def twitter_setup():\n",
    "    \"\"\"\n",
    "    Utility function to setup the Twitter's API\n",
    "    with our access keys provided.\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",
    "    # Return API with authentication:\n",
    "    api = tweepy.API(auth)\n",
    "    return api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unless you want a data for different user, keep the preset value in the code below:\n",
    "\n",
    "TW_USER - this is the username of the account you want to scrape\n",
    "\n",
    "TARGET_COUNT - this is the total number of tweets to download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TW_USER='elonmusk'\n",
    "TARGET_COUNT=200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can run the code below to actually download the data from the Twitter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tweets extracted: 199.\n",
      "\n",
      "5 recent tweets:\n",
      "\n",
      "Space Ark - 1968 by Japanese artist Shigeru Komatsuzaki (1915-2001)\n",
      "\n",
      "Starship takes beings of Earth to Mars https://t.co/6qaIc3p4yA\n",
      "\n",
      "@CChomp13 🤣💯\n",
      "\n",
      "@sama What’s the average cost per chat?\n",
      "\n",
      "@WholeMarsBlog Electric cargo ships are straightforward, as are short to medium range electric aircraft. Long-range… https://t.co/4t7raza695\n",
      "\n"
     ]
    }
   ],
   "source": [
    "extractor = twitter_setup()\n",
    "\n",
    "tweets = extractor.user_timeline(screen_name=TW_USER, TARGET_COUNT)\n",
    "print(\"Number of tweets extracted: {}.\\n\".format(len(tweets)))\n",
    "\n",
    "print(\"5 recent tweets:\\n\")\n",
    "for tweet in tweets[:5]:\n",
    "    print(tweet.text)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step is to extract the text part from the tweets - starting with \"dataframe\" creation:"
   ]
  },
  {
   "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",
       "    }\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>Space Ark - 1968 by Japanese artist Shigeru Ko...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Starship takes beings of Earth to Mars https:/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@CChomp13 🤣💯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@sama What’s the average cost per chat?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@WholeMarsBlog Electric cargo ships are straig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@BillyM2k Team was a bit too intense with spam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>@RichardGarriott 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@WholeMarsBlog About a week or so</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>@COLDEX_STC @SpaceX @NSF @blueicehiggins @icy_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @COLDEX_STC: Despite 30 knot winds at the A...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets\n",
       "0  Space Ark - 1968 by Japanese artist Shigeru Ko...\n",
       "1  Starship takes beings of Earth to Mars https:/...\n",
       "2                                       @CChomp13 🤣💯\n",
       "3            @sama What’s the average cost per chat?\n",
       "4  @WholeMarsBlog Electric cargo ships are straig...\n",
       "5  @BillyM2k Team was a bit too intense with spam...\n",
       "6                                 @RichardGarriott 1\n",
       "7                  @WholeMarsBlog About a week or so\n",
       "8  @COLDEX_STC @SpaceX @NSF @blueicehiggins @icy_...\n",
       "9  RT @COLDEX_STC: Despite 30 knot winds at the A..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for your reference - browse all the avalaible methods in the api. You can see you can also exctract location and other information:"
   ]
  },
  {
   "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__', '__slots__', '__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": [
    "\n",
    "print(dir(tweets[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examples of what does the other methods data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1599672158086889474\n",
      "2022-12-05 07:48:57+00:00\n",
      "Twitter for iPhone\n",
      "46373\n",
      "2583\n",
      "None\n",
      "None\n",
      "{'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': []}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "Now we load the relevant data to the table \"data\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "See the first 10 elements of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Space Ark - 1968 by Japanese artist Shigeru Ko...</td>\n",
       "      <td>67</td>\n",
       "      <td>1599672158086889474</td>\n",
       "      <td>2022-12-05 07:48:57+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>46373</td>\n",
       "      <td>2583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Starship takes beings of Earth to Mars https:/...</td>\n",
       "      <td>62</td>\n",
       "      <td>1599671964582391808</td>\n",
       "      <td>2022-12-05 07:48:11+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>148985</td>\n",
       "      <td>12713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@CChomp13 🤣💯</td>\n",
       "      <td>12</td>\n",
       "      <td>1599669828478185472</td>\n",
       "      <td>2022-12-05 07:39:42+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>4189</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@sama What’s the average cost per chat?</td>\n",
       "      <td>39</td>\n",
       "      <td>1599669552081960960</td>\n",
       "      <td>2022-12-05 07:38:36+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>22860</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@WholeMarsBlog Electric cargo ships are straig...</td>\n",
       "      <td>140</td>\n",
       "      <td>1599643371428986880</td>\n",
       "      <td>2022-12-05 05:54:34+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>18147</td>\n",
       "      <td>1377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@BillyM2k Team was a bit too intense with spam...</td>\n",
       "      <td>85</td>\n",
       "      <td>1599640228721233921</td>\n",
       "      <td>2022-12-05 05:42:05+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>19394</td>\n",
       "      <td>1110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>@RichardGarriott 1</td>\n",
       "      <td>18</td>\n",
       "      <td>1599638758697013248</td>\n",
       "      <td>2022-12-05 05:36:14+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>43137</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@WholeMarsBlog About a week or so</td>\n",
       "      <td>33</td>\n",
       "      <td>1599628966398025728</td>\n",
       "      <td>2022-12-05 04:57:19+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>4318</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>@COLDEX_STC @SpaceX @NSF @blueicehiggins @icy_...</td>\n",
       "      <td>140</td>\n",
       "      <td>1599628470744588288</td>\n",
       "      <td>2022-12-05 04:55:21+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>19229</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @COLDEX_STC: Despite 30 knot winds at the A...</td>\n",
       "      <td>140</td>\n",
       "      <td>1599628033962307584</td>\n",
       "      <td>2022-12-05 04:53:37+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>3865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets  len  \\\n",
       "0  Space Ark - 1968 by Japanese artist Shigeru Ko...   67   \n",
       "1  Starship takes beings of Earth to Mars https:/...   62   \n",
       "2                                       @CChomp13 🤣💯   12   \n",
       "3            @sama What’s the average cost per chat?   39   \n",
       "4  @WholeMarsBlog Electric cargo ships are straig...  140   \n",
       "5  @BillyM2k Team was a bit too intense with spam...   85   \n",
       "6                                 @RichardGarriott 1   18   \n",
       "7                  @WholeMarsBlog About a week or so   33   \n",
       "8  @COLDEX_STC @SpaceX @NSF @blueicehiggins @icy_...  140   \n",
       "9  RT @COLDEX_STC: Despite 30 knot winds at the A...  140   \n",
       "\n",
       "                    ID                      Date              Source   Likes  \\\n",
       "0  1599672158086889474 2022-12-05 07:48:57+00:00  Twitter for iPhone   46373   \n",
       "1  1599671964582391808 2022-12-05 07:48:11+00:00  Twitter for iPhone  148985   \n",
       "2  1599669828478185472 2022-12-05 07:39:42+00:00  Twitter for iPhone    4189   \n",
       "3  1599669552081960960 2022-12-05 07:38:36+00:00  Twitter for iPhone   22860   \n",
       "4  1599643371428986880 2022-12-05 05:54:34+00:00  Twitter for iPhone   18147   \n",
       "5  1599640228721233921 2022-12-05 05:42:05+00:00  Twitter for iPhone   19394   \n",
       "6  1599638758697013248 2022-12-05 05:36:14+00:00  Twitter for iPhone   43137   \n",
       "7  1599628966398025728 2022-12-05 04:57:19+00:00  Twitter for iPhone    4318   \n",
       "8  1599628470744588288 2022-12-05 04:55:21+00:00  Twitter for iPhone   19229   \n",
       "9  1599628033962307584 2022-12-05 04:53:37+00:00  Twitter for iPhone       0   \n",
       "\n",
       "     RTs  \n",
       "0   2583  \n",
       "1  12713  \n",
       "2    191  \n",
       "3    764  \n",
       "4   1377  \n",
       "5   1110  \n",
       "6   1260  \n",
       "7    210  \n",
       "8    918  \n",
       "9   3865  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the average length of the tweets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lenght's average in tweets: 70.5929648241206\n"
     ]
    }
   ],
   "source": [
    "mean = np.mean(data['len'])\n",
    "print(\"The lenght's average in tweets: {}\".format(mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the likes and retweets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tweet with more likes is: \n",
      "This is a battle for the future of civilization. If free speech is lost even in America, tyranny is all that lies ahead.\n",
      "Number of likes: 921843\n",
      "120 characters.\n",
      "\n",
      "The tweet with more retweets is: \n",
      "This is a battle for the future of civilization. If free speech is lost even in America, tyranny is all that lies ahead.\n",
      "Number of retweets: 146407\n",
      "120 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",
    "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",
    "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": "markdown",
   "metadata": {},
   "source": [
    "To be able to view the time series, we have to add the \"Date\" information to the table:"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "Length over the time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tlen.plot(figsize=(16,4), color='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likes vs. Retweets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "What is the source of the Tweet? Mobile app vs web vs application?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creation of content sources:\n",
      "* Twitter for iPhone\n"
     ]
    }
   ],
   "source": [
    "sources = []\n",
    "for source in data['Source']:\n",
    "    if source not in sources:\n",
    "        sources.append(source)\n",
    "\n",
    "print(\"Creation of content sources:\")\n",
    "for source in sources:\n",
    "    print(\"* {}\".format(source))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cake view for the Tweet source distribution: This is handy to see if the content is geenric or created via API / App"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We create a numpy vector mapped to labels:\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "Sentiment analysis:\n",
    "\n",
    "Now that we created the dataset with the texts of the tweets and date, it is possible to run the sentiment alaysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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 analyze_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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample of results:\n",
    "'SA' stands for sentiment analysis. Note that value for the sentiment is rounded for clearer view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Space Ark - 1968 by Japanese artist Shigeru Ko...</td>\n",
       "      <td>67</td>\n",
       "      <td>1599672158086889474</td>\n",
       "      <td>2022-12-05 07:48:57+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>46373</td>\n",
       "      <td>2583</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Starship takes beings of Earth to Mars https:/...</td>\n",
       "      <td>62</td>\n",
       "      <td>1599671964582391808</td>\n",
       "      <td>2022-12-05 07:48:11+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>148985</td>\n",
       "      <td>12713</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@CChomp13 🤣💯</td>\n",
       "      <td>12</td>\n",
       "      <td>1599669828478185472</td>\n",
       "      <td>2022-12-05 07:39:42+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>4189</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@sama What’s the average cost per chat?</td>\n",
       "      <td>39</td>\n",
       "      <td>1599669552081960960</td>\n",
       "      <td>2022-12-05 07:38:36+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>22860</td>\n",
       "      <td>764</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@WholeMarsBlog Electric cargo ships are straig...</td>\n",
       "      <td>140</td>\n",
       "      <td>1599643371428986880</td>\n",
       "      <td>2022-12-05 05:54:34+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>18147</td>\n",
       "      <td>1377</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@BillyM2k Team was a bit too intense with spam...</td>\n",
       "      <td>85</td>\n",
       "      <td>1599640228721233921</td>\n",
       "      <td>2022-12-05 05:42:05+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>19394</td>\n",
       "      <td>1110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>@RichardGarriott 1</td>\n",
       "      <td>18</td>\n",
       "      <td>1599638758697013248</td>\n",
       "      <td>2022-12-05 05:36:14+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>43137</td>\n",
       "      <td>1260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@WholeMarsBlog About a week or so</td>\n",
       "      <td>33</td>\n",
       "      <td>1599628966398025728</td>\n",
       "      <td>2022-12-05 04:57:19+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>4318</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>@COLDEX_STC @SpaceX @NSF @blueicehiggins @icy_...</td>\n",
       "      <td>140</td>\n",
       "      <td>1599628470744588288</td>\n",
       "      <td>2022-12-05 04:55:21+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>19229</td>\n",
       "      <td>918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @COLDEX_STC: Despite 30 knot winds at the A...</td>\n",
       "      <td>140</td>\n",
       "      <td>1599628033962307584</td>\n",
       "      <td>2022-12-05 04:53:37+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>3865</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets  len  \\\n",
       "0  Space Ark - 1968 by Japanese artist Shigeru Ko...   67   \n",
       "1  Starship takes beings of Earth to Mars https:/...   62   \n",
       "2                                       @CChomp13 🤣💯   12   \n",
       "3            @sama What’s the average cost per chat?   39   \n",
       "4  @WholeMarsBlog Electric cargo ships are straig...  140   \n",
       "5  @BillyM2k Team was a bit too intense with spam...   85   \n",
       "6                                 @RichardGarriott 1   18   \n",
       "7                  @WholeMarsBlog About a week or so   33   \n",
       "8  @COLDEX_STC @SpaceX @NSF @blueicehiggins @icy_...  140   \n",
       "9  RT @COLDEX_STC: Despite 30 knot winds at the A...  140   \n",
       "\n",
       "                    ID                      Date              Source   Likes  \\\n",
       "0  1599672158086889474 2022-12-05 07:48:57+00:00  Twitter for iPhone   46373   \n",
       "1  1599671964582391808 2022-12-05 07:48:11+00:00  Twitter for iPhone  148985   \n",
       "2  1599669828478185472 2022-12-05 07:39:42+00:00  Twitter for iPhone    4189   \n",
       "3  1599669552081960960 2022-12-05 07:38:36+00:00  Twitter for iPhone   22860   \n",
       "4  1599643371428986880 2022-12-05 05:54:34+00:00  Twitter for iPhone   18147   \n",
       "5  1599640228721233921 2022-12-05 05:42:05+00:00  Twitter for iPhone   19394   \n",
       "6  1599638758697013248 2022-12-05 05:36:14+00:00  Twitter for iPhone   43137   \n",
       "7  1599628966398025728 2022-12-05 04:57:19+00:00  Twitter for iPhone    4318   \n",
       "8  1599628470744588288 2022-12-05 04:55:21+00:00  Twitter for iPhone   19229   \n",
       "9  1599628033962307584 2022-12-05 04:53:37+00:00  Twitter for iPhone       0   \n",
       "\n",
       "     RTs  SA  \n",
       "0   2583   0  \n",
       "1  12713   0  \n",
       "2    191   0  \n",
       "3    764  -1  \n",
       "4   1377   1  \n",
       "5   1110   1  \n",
       "6   1260   0  \n",
       "7    210   0  \n",
       "8    918   0  \n",
       "9   3865   1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['SA'] = np.array([ analyze_sentiment(tweet) for tweet in data['Tweets'] ])\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcualtion of the percentage of Positive, Neutral or Negative tweets: \n",
    "\n",
    "This can show you if the account is the real user or just a spam bot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of positive tweets: 44.72361809045226%\n",
      "Percentage of neutral tweets: 44.72361809045226%\n",
      "Percentage de negative tweets: 10.552763819095478%\n"
     ]
    }
   ],
   "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]\n",
    "\n",
    "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'])))"
   ]
  }
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