Data_Pull.py

import csv
import tweepy
import numpy as np
from textblob import TextBlob
import datetime
import time

def main():

    # set twitter api credentials
    consumer_key= 'MOe623rxcqck6x8y5XhzK8MJT'
    consumer_secret= 'mcBq9Km1f3OYERRD6vKmOfWSgCjsqzXAreIsn8klxAtPIo40E7'
    access_token='913787859630460928-RXF8NVN3gGbxD64NCZ7wBma5M2WPwlv'
    access_token_secret='P4UU9I2DimdnUown2EM6p4WZ0ftdPNsysDNW6xGh0Ts4f'

    # set path of csv file to save sentiment stats
    path = 'live_tweet.csv'
    f = open(path,"a")
    f1 = open('tweet_data','a', encoding="utf-8")
    # access twitter api via tweepy methods
    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
    auth.set_access_token(access_token, access_token_secret)
    twitter_api = tweepy.API(auth)

    while True:

        # fetch tweets by keywords
        tweets = twitter_api.search(q=['bitcoin, price, crypto'], count=100)

        # get polarity
        polarity = get_polarity(tweets,f1)
        sentiment = np.mean(polarity)

        # save sentiment data to csv file
        f.write(str(sentiment))
        f.write(","+datetime.datetime.now().strftime("%y-%m-%d-%H-%M"))
        f.write("\n")
        f.flush()
        time.sleep(60)
    

def get_polarity(tweets,f):
    # run polarity analysis on tweets

    tweet_polarity = []

    for tweet in tweets:
        f.write(tweet.text+'\n')
        analysis = TextBlob(tweet.text)
        tweet_polarity.append(analysis.sentiment.polarity)

    return tweet_polarity

main()