05_twitter_data_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# TWITTER WEBSCRAPPING\n",
    "\n",
    "Here we are going to webscrap the twitter accounts of the top-100 influencers in the cryptocurrency world. The first step is webscrapping the top100 accounts and then all the tweets they perfromed.\n",
    "\n",
    "#### Data Sources:\n",
    "\n",
    "Cryptocurrencies influencer ranking: \"https://cryptoweekly.co/100/\"\n",
    "\n",
    "The following code has been adapted from:\n",
    "https://codeburst.io/a-twitter-analysis-of-the-100-most-influential-people-in-crypto-bb95b2608925 and https://dev.to/rodolfoferro/sentiment-analysis-on-trumpss-tweets-using-python-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First we load the general packages:\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here we are going to write a function to webscarpe the names of the influencers and their twitter handles:\n",
    "\n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup as soup\n",
    "\n",
    "def getTwitterHandles():\n",
    "\t# Fill in with url of page which is to be scraped\n",
    "\turl = \"https://cryptoweekly.co/100/\"\n",
    "\n",
    "\t# Retreives and parses page html\n",
    "\tclient = urlopen(url)\n",
    "\tpageHtml = client.read()\n",
    "\tpageSoup = soup(pageHtml, \"html.parser\")\n",
    "\n",
    "\t# Adds all Twitter handles to twitterHandles list\n",
    "\tprofiles = pageSoup.findAll(\"div\", {\"class\":\"testimonial-wrapper\"})\n",
    "\ttwitterHandles = []\n",
    "\tfor person in profiles:\n",
    "\t\ttwitterHandles.append(person.findAll(\"div\",{\"class\":\"author\"}))\n",
    "\tfor i in range(len(twitterHandles)):\n",
    "\t\ttwitterHandles[i]=twitterHandles[i][0].findAll(\"a\")[0].text[1:]\n",
    "\n",
    "\tclient.close()\n",
    "\treturn twitterHandles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting tweets from @VitalikButerin\n",
      "Getting tweets before 1030153461071937535\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...2194 tweets downloaded so far\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...1399 tweets downloaded so far\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...1594 tweets downloaded so far\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...1599 tweets downloaded so far\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...999 tweets downloaded so far\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...999 tweets downloaded so far\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "...2980 tweets downloaded so far\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...400 tweets downloaded so far\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...1400 tweets downloaded so far\n",
      "Getting tweets before 887797322809171967\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...2000 tweets downloaded so far\n",
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      "...3200 tweets downloaded so far\n",
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      "Getting tweets before 938111754831196159\n",
      "...3225 tweets downloaded so far\n",
      "Getting tweets from @alexsunnarborg\n",
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      "...400 tweets downloaded so far\n",
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      "...882 tweets downloaded so far\n",
      "Getting tweets from @TonyGallippi\n",
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      "Getting tweets before 554836452476190719\n",
      "...3156 tweets downloaded so far\n",
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      "...3156 tweets downloaded so far\n",
      "Getting tweets from @AnselLindner\n",
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      "...3197 tweets downloaded so far\n",
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      "...3197 tweets downloaded so far\n",
      "Getting tweets from @dieguito\n",
      "Getting tweets before 1034468186564042751\n",
      "...400 tweets downloaded so far\n",
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      "...1800 tweets downloaded so far\n",
      "Getting tweets before 965720960077910015\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...2000 tweets downloaded so far\n",
      "Getting tweets before 959166162356629504\n",
      "...2200 tweets downloaded so far\n",
      "Getting tweets before 949810112918687743\n",
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      "Getting tweets before 911394006696910848\n",
      "...3234 tweets downloaded so far\n",
      "Getting tweets before 909490641259962370\n",
      "...3234 tweets downloaded so far\n",
      "Getting tweets from @certainassets\n",
      "Getting tweets before 955916091066339328\n",
      "...395 tweets downloaded so far\n",
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      "Getting tweets before 684352199984545791\n",
      "...1465 tweets downloaded so far\n",
      "Getting tweets before 650360417475264511\n",
      "...1465 tweets downloaded so far\n",
      "Getting tweets from @joonian\n",
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      "...397 tweets downloaded so far\n",
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      "Getting tweets from @roasbeef\n",
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      "...3244 tweets downloaded so far\n"
     ]
    }
   ],
   "source": [
    "# Here the Twitter API tweepy is used to webscrape the tweets from the influencers:\n",
    "\n",
    "import tweepy\n",
    "import csv\n",
    "import sys\n",
    "\n",
    "# Twitter API credentials (expired, don't even try it)\n",
    "consumer_key = \"\"\n",
    "consumer_secret = \"\"\n",
    "access_key = \"\"\n",
    "access_secret = \"\"\n",
    "\n",
    "\n",
    "def get_all_tweets(screen_name):\n",
    "\tprint(\"Getting tweets from @\" + str(screen_name))\n",
    "\n",
    "\t#Twitter only allows access to a users most recent 3240 tweets with this method\n",
    "\t\n",
    "\t#authorize twitter, initialize tweepy\n",
    "\tauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "\tauth.set_access_token(access_key, access_secret)\n",
    "\tapi = tweepy.API(auth)\n",
    "\t\n",
    "\t#initialize a list to hold all the tweepy Tweets\n",
    "\talltweets = []\t\n",
    "\t\n",
    "\t#make initial request for most recent tweets (200 is the maximum allowed count)\n",
    "\tnew_tweets = api.user_timeline(screen_name = screen_name,count=200)\n",
    "\t\n",
    "\t#save most recent tweets\n",
    "\talltweets.extend(new_tweets)\n",
    "\t\n",
    "\t#save the id of the oldest tweet less one\n",
    "\toldest = alltweets[-1].id - 1\n",
    "\t\n",
    "\t#keep grabbing tweets until there are no tweets left to grab\n",
    "\twhile len(new_tweets) > 0:\n",
    "\t\tprint (\"Getting tweets before %s\" % (oldest))\n",
    "\t\t\n",
    "\t\t#all subsiquent requests use the max_id param to prevent duplicates\n",
    "\t\tnew_tweets = api.user_timeline(screen_name = screen_name,count=200,max_id=oldest)\n",
    "\t\t\n",
    "\t\t#save most recent tweets\n",
    "\t\talltweets.extend(new_tweets)\n",
    "\t\t\n",
    "\t\t#update the id of the oldest tweet less one\n",
    "\t\toldest = alltweets[-1].id - 1\n",
    "\t\t\n",
    "\t\tprint (\"...%s tweets downloaded so far\" % (len(alltweets)))\n",
    "\n",
    "\t#transform the tweepy tweets into a 2D array that will populate the csv\t\n",
    "\touttweets = [[tweet.id_str.encode('utf-8'), tweet.created_at.strftime('%m/%d/%Y'), tweet.text.encode('utf-8')] for tweet in alltweets]\n",
    "\t\n",
    "\t#write the csv\t\n",
    "\twith open('Data/Tweets/%s_tweets.csv' % screen_name, 'w') as f:\n",
    "\t\twriter = csv.writer(f)\n",
    "\t\twriter.writerow([\"id\",\"created_at\",\"text\"])\n",
    "\t\twriter.writerows(outtweets)\n",
    "\t\n",
    "\tpass\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\thandles = getTwitterHandles()\n",
    "    # The Peter Todd twitter handle is updated to his new account, as long as the old one was listed in the website:\n",
    "\tfor i in range(len(handles)):\n",
    "\t\tif handles[i] == 'petertoddbtc':\n",
    "\t\t\thandles[i] = 'peterktodd'\n",
    "\tfor handle in handles:\n",
    "\t\tget_all_tweets(str(handle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The previous function generated a .csv file for every account, here we create a unified dataset:\n",
    "# Create a unified dataset:\n",
    "mt = pd.DataFrame(columns = [\"Name\", \"id\", \"created_at\", \"text\"])\n",
    "\n",
    "for handle in handles:\n",
    "    df = pd.read_csv(\"Data/Tweets/\"+str(handle)+\"_tweets.csv\")\n",
    "    df['Name'] = handle\n",
    "    df = df[[\"Name\", \"id\", \"created_at\", \"text\"]]\n",
    "    mt = mt.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame.to_csv(mt, \"Data/twitter_ddbb.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## TWITTER SENTIMENT ANALYSIS\n",
    "\n",
    "from textblob import TextBlob\n",
    "import re\n",
    "\n",
    "def clean_tweet(tweet):\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",
    "    analysis = TextBlob(clean_tweet(tweet))\n",
    "    return analysis.sentiment.polarity    \n",
    "    \n",
    "# We create a column with the result of the analysis:\n",
    "mt['SA'] = np.array([ analize_sentiment(tweet) for tweet in mt['text']])\n",
    "\n",
    "# We display the updated dataframe with the new column:\n",
    "#display(mt.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame.to_csv(mt, \"Data/twitter_ddbb_SA.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's reaload the data again, for further preprocessing without need of running the scrape and NLP processes every time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mt = pd.read_csv(\"Data/twitter_ddbb_SA.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Selecting only the tweets in the desired time frame:\n",
    "mt['created_at'] = pd.to_datetime(mt['created_at'])\n",
    "mask = (mt['created_at'] >= '05/01/2017') & (mt['created_at'] <= '05/01/2018')\n",
    "mt.loc[mask]\n",
    "twind_mt = mt.loc[mask]\n",
    "\n",
    "# Group by DAY and aggreagate the data:\n",
    "agg_mt = twind_mt.groupby('created_at').agg({'text':'count', 'Name': 'nunique', 'SA': 'mean'})\n",
    "agg_mt = agg_mt.rename(columns={'text': 'Tweets (#)', 'Name': 'Active Influencers (#)', 'SA': 'Average SA'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame.to_csv(agg_mt, \"Data/twitter_agg_ddb.csv\")"
   ]
  }
 ],
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