Using various Python libraries such as Pandas, tweetPy, JSON and matplotLib to take a sneak peek on your Twitter account using Google Colab.
from google.colab import drive # to mount Google Drive to Colab notebook
import tweepy # Python wrapper around Twitter API
import json
import pandas as pd
import csv
from datetime import date
from datetime import datetime
import time
import matplotlib.pyplot as pltcreate a new folder name it dataset then another inside it with twitter_analysis Yo can change the directory as you like
drive.mount('/content/gdrive')
path = './gdrive/My Drive/datasets/twitter_analysis/'
#Follow the popup link and complete the autuorization processIn this section you need to get your Twitter API credentials then load Twitter API secrets from an external file
secrets = json.loads(open(path + 'secrets.json').read())
consumer_key = secrets['consumer_key']
consumer_secret = secrets['consumer_secret']
access_token = secrets['access_token']
access_token_secret = secrets['access_token_secret']
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)save_json(file_name, file_content): Helper function to save data into a JSON file.file_name: the name of the data file you want to save on your Google Drivefile_content: the data you want to save.limit_handled(cursor, list_name)Helper function to handle twitter API rate limit.
get_all_tweets(screen_name): Helper function to get all tweets for a specified user and write it to a csv file.todays_stats(dict_name): Helper function to get today's numbers of followers and friends and store them into a JSON file.get_followers(): Helper function to load follower objects into a list and save them into a JSON file.get_friends(): Load friends into list
- Load saved data from Google Drive
- Classify tweets
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Analyzing followers
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Analyzing following
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Analyzing History