scraptweets.py

def scraptweets(search_words, date_since, numTweets, numRuns):

    ## Arguments:
    # search_words -> define a string of keywords for this function to extract
    # date_since -> define a date from which to start extracting the tweets 
    # numTweets -> number of tweets to extract per run
    # numRun -> number of runs to perform in this program - API calls are limited to once every 15 mins, so each run will be 15 mins apart.
    ##
    from tweepy import OAuthHandler
    from tweepy.streaming import StreamListener
    import tweepy
    import json
    import pandas as pd
    import csv
    import re
    from textblob import TextBlob
    import string
    import preprocessor as p
    import os
    import time
    
    # Define a pandas dataframe to store the date:
    db_tweets = pd.DataFrame(columns = ['username', 'acctdesc', 'location', 'following',
                                        'followers', 'totaltweets', 'usercreatedts', 'tweetcreatedts',
                                        'retweetcount', 'text', 'hashtags']
                                )
    # Define a for-loop to generate tweets at regular intervals
    for i in range(0, numRuns):
        # We will time how long it takes to scrape tweets for each run:
        start_run = time.time()
        
        # Collect tweets using the Cursor object
        # .Cursor() returns an object that you can iterate or loop over to access the data collected.
        # Each item in the iterator has various attributes that you can access to get information about each tweet
        tweets = tweepy.Cursor(api.search, q=search_words, lang="hi", since=date_since, tweet_mode='extended').items(numTweets)

        # Store these tweets into a python list
        tweet_list = [tweet for tweet in tweets]

        # Obtain the following info (methods to call them out):
            # user.screen_name - twitter handle
            # user.description - description of account
            # user.location - where is he tweeting from
            # user.friends_count - no. of other users that user is following (following)
            # user.followers_count - no. of other users who are following this user (followers)
            # user.statuses_count - total tweets by user
            # user.created_at - when the user account was created
            # created_at - when the tweet was created
            # retweet_count - no. of retweets
            # (deprecated) user.favourites_count - probably total no. of tweets that is favourited by user
            # retweeted_status.full_text - full text of the tweet
            # tweet.entities['hashtags'] - hashtags in the tweet

        # Begin scraping the tweets individually:
        noTweets = 0

        for tweet in tweet_list:

            # Pull the values
            username = tweet.user.screen_name
            acctdesc = tweet.user.description
            location = tweet.user.location
            following = tweet.user.friends_count
            followers = tweet.user.followers_count
            totaltweets = tweet.user.statuses_count
            usercreatedts = tweet.user.created_at
            tweetcreatedts = tweet.created_at
            retweetcount = tweet.retweet_count
            hashtags = tweet.entities['hashtags']

            try:
                text = tweet.retweeted_status.full_text
            except AttributeError:  # Not a Retweet
                text = tweet.full_text

            # Add the 11 variables to the empty list - ith_tweet:
            ith_tweet = [username, acctdesc, location, following, followers, totaltweets,
                         usercreatedts, tweetcreatedts, retweetcount, text, hashtags]

            # Append to dataframe - db_tweets
            db_tweets.loc[len(db_tweets)] = ith_tweet

            # increase counter - noTweets  
            noTweets += 1
        
        # Run ended:
        end_run = time.time()
        duration_run = round(end_run-start_run, 2)
        
        print('no. of tweets scraped for run {} is {}'.format(i, noTweets))
        print('time take for {} run to complete is {}'.format(i, duration_run))
        
        time.sleep(900) #15 minute sleep time

        
    # Once all runs have completed, save them to a single csv file:    
    # Obtain timestamp in a readable format:
    from datetime import datetime
    to_csv_timestamp = datetime.today().strftime('%Y%m%d_%H%M%S')

    # Define working path and filename
    path = os.getcwd()
    filename = 'Sarcasm_Hindi_Tweets.csv'

    # Store dataframe in csv with creation date timestamp
    db_tweets.to_csv(filename, index = False)
    
    print('Scraping has completed!')