Historical Twitter Sentiment Analyzer. Read more here - https://aksmtr.com/articles/9-analyze-twitter-sentiment-trend-using-python-flair

TwitterSentimentAnalyzer.py

import re
import tweepy
import flair
import datetime

class TwitterSentimentAnalyzer:
    twitter_api = None
    whitespace = re.compile(r"\s+")
    web_address = re.compile(r"(?i)http(s):\/\/[a-z0-9.~_\-\/]+")
    user = re.compile(r"(?i)@[a-z0-9_]+")
    sentiment_model = flair.models.TextClassifier.load('en-sentiment')

    def __init__(self, bearer_token):
        auth = tweepy.OAuth2BearerHandler(bearer_token)
        self.twitter_api = tweepy.API(auth,
                                      wait_on_rate_limit=True,
                                      retry_count=10,
                                      retry_delay=30)

    def __search_twitter_history(self, topic, start_date_str, end_date_str):
        start = datetime.datetime.strptime(start_date_str, '%Y-%m-%d')
        end = datetime.datetime.strptime(end_date_str, '%Y-%m-%d')
        date_obj = start
        results = []
        while(date_obj <= end):
            next_day = date_obj + datetime.timedelta(days=1)
            query = topic + " -filter:retweets" \
                    + " since:" + date_obj.strftime('%Y-%m-%d') \
                    + " until:" + next_day.strftime('%Y-%m-%d')
            tweets = tweepy.Cursor(self.twitter_api.search_tweets,
                                   q=query, lang='en', count=100,
                                   tweet_mode='extended').items()
            results.append({
                'day': date_obj.strftime('%Y-%m-%d'),
                'tweets': tweets
            })
            date_obj = next_day
        return results

    def __clean_tweet(self, text):
        text = self.whitespace.sub(' ', text)
        text = self.web_address.sub('', text)
        text = self.user.sub('', text)
        return text

    def __analyze(self, text):
        sentence = flair.data.Sentence(text)
        self.sentiment_model.predict(sentence)
        return sentence

    def __get_sentiment(self, tweets, dt):
        results = []
        for tweet in tweets:
            if tweet.lang != 'en':
                continue
            cleaned_tweet = self.__clean_tweet(tweet.full_text)
            s = self.__analyze(cleaned_tweet)
            results.append({
                "date": dt,
                "tweet": tweet.full_text,
                "sentiment": s.labels[0].value,
                "score": s.labels[0].score * (1 if s.labels[0].value == 'POSITIVE' else -1),
                "lang": tweet.lang
            })
        return results

    def historical_sentiment(self, term, start_dt, end_dt):
        tweets_across_days = self.__search_twitter_history(term, start_dt, end_dt)
        results = {}
        for day_tweets in tweets_across_days:
            results [day_tweets['day']] = self.__get_sentiment(day_tweets['tweets'], day_tweets['day'])
        return results