tweetsendbot.py

import tweepy
import re

from telegram.ext import Updater, MessageHandler, Filters
from google.cloud import language
from google.cloud.language import enums
from google.cloud.language import types
from datetime import datetime, timedelta
from nltk.tokenize import WordPunctTokenizer


ACC_TOKEN = 'YOUR_ACCESS_TOKEN'
ACC_SECRET = 'YOUR_ACCESS_TOKEN_SECRET'
CONS_KEY = 'YOUR_CONSUMER_API_KEY'
CONS_SECRET = 'YOUR_CONSUMER_API_SECRET_KEY'

def authentication(cons_key, cons_secret, acc_token, acc_secret):
    auth = tweepy.OAuthHandler(cons_key, cons_secret)
    auth.set_access_token(acc_token, acc_secret)
    api = tweepy.API(auth)
    return api

def search_tweets(keyword, total_tweets):
    today_datetime = datetime.today().now()
    yesterday_datetime = today_datetime - timedelta(days=1)
    today_date = today_datetime.strftime('%Y-%m-%d')
    yesterday_date = yesterday_datetime.strftime('%Y-%m-%d')
    api = authentication(CONS_KEY,CONS_SECRET,ACC_TOKEN,ACC_SECRET)
    search_result = tweepy.Cursor(api.search, 
                                  q=keyword, 
                                  since=yesterday_date, 
                                  result_type='recent', 
                                  lang='en').items(total_tweets)
    return search_result

def clean_tweets(tweet):
    user_removed = re.sub(r'@[A-Za-z0-9]+','',tweet.decode('utf-8'))
    link_removed = re.sub('https?://[A-Za-z0-9./]+','',user_removed)
    number_removed = re.sub('[^a-zA-Z]', ' ', link_removed)
    lower_case_tweet= number_removed.lower()
    tok = WordPunctTokenizer()
    words = tok.tokenize(lower_case_tweet)
    clean_tweet = (' '.join(words)).strip()
    return clean_tweet

def get_sentiment_score(tweet):
    client = language.LanguageServiceClient()
    document = types\
               .Document(content=tweet,
                         type=enums.Document.Type.PLAIN_TEXT)
    sentiment_score = client\
                      .analyze_sentiment(document=document)\
                      .document_sentiment\
                      .score
    return sentiment_score

def analyze_tweets(keyword, total_tweets):
    score = 0
    tweets = search_tweets(keyword,total_tweets)
    for tweet in tweets:
        cleaned_tweet = clean_tweets(tweet.text.encode('utf-8'))
        sentiment_score = get_sentiment_score(cleaned_tweet)
        score += sentiment_score
        print('Tweet: {}'.format(cleaned_tweet))
        print('Score: {}\n'.format(sentiment_score))
    final_score = round((score / float(total_tweets)),2)
    return final_score

def send_the_result(bot, update):
    keyword = update.message.text
    final_score = analyze_tweets(keyword, 50)
    if final_score <= -0.25:
        status = 'NEGATIVE | ❌'
    elif final_score <= 0.25:
        status = 'NEUTRAL | 🔶'
    else:
        status = 'POSITIVE | ✅'
    bot.send_message(chat_id=update.message.chat_id,
                     text='Average score for '
                           + str(keyword) 
                           + ' is ' 
                           + str(final_score) 
                           + ' | ' + status)

def main():
    updater = Updater('YOUR_TOKEN')
    dp = updater.dispatcher
    dp.add_handler(MessageHandler(Filters.text, send_the_result))
    updater.start_polling()
    updater.idle()

if __name__ == '__main__':
    main()