| import streamlit as st | |
| import tweepy as tw | |
| import pandas as pd | |
| from transformers import pipeline | |
| consumer_key = 'OCgWzDW6PaBvBeVimmGBqdAg1' | |
| consumer_secret = 'tBKnmyg5Jfsewkpmw74gxHZbbZkGIH6Ee4rsM0lD1vFL7SrEIM' | |
| access_token = '1449663645412065281-LNjZoEO9lxdtxPcmLtM35BRdIKYHpk' | |
| access_token_secret = 'FL3SGsUWSzPVFnG7bNMnyh4vYK8W1SlABBNtdF7Xcbh7a' | |
| auth = tw.OAuthHandler(consumer_key, consumer_secret) | |
| auth.set_access_token(access_token, access_token_secret) | |
| api = tw.API(auth, wait_on_rate_limit=True) | |
| classifier = pipeline('sentiment-analysis') | |
| st.title('Live Twitter Sentiment Analysis with Tweepy and HuggingFace Transformers') | |
| st.markdown('This app uses tweepy to get tweets from twitter based on the input name/phrase. It then processes the tweets through HuggingFace transformers pipeline function for sentiment analysis. The resulting sentiments and corresponding tweets are then put in a dataframe for display which is what you see as result.') | |
| def run(): | |
| with st.form(key='Enter name'): | |
| search_words = st.text_input('Enter the name for which you want to know the sentiment') | |
| number_of_tweets = st.number_input('Enter the number of latest tweets for which you want to know the sentiment(Maximum 50 tweets)', 0,50,10) | |
| submit_button = st.form_submit_button(label='Submit') | |
| if submit_button: | |
| tweets =tw.Cursor(api.search_tweets,q=search_words,lang="en").items(number_of_tweets) | |
| tweet_list = [i.text for i in tweets] | |
| p = [i for i in classifier(tweet_list)] | |
| q=[p[i]['label'] for i in range(len(p))] | |
| df = pd.DataFrame(list(zip(tweet_list, q)),columns =['Latest '+str(number_of_tweets)+' Tweets'+' on '+search_words, 'sentiment']) | |
| st.write(df) | |
| if __name__=='__main__': | |
| run() |