muskangoyal06 commited on
Commit
c4d773d
·
verified ·
1 Parent(s): 9ddbc59

Update app.py

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Files changed (1) hide show
  1. app.py +13 -13
app.py CHANGED
@@ -9,14 +9,9 @@ import daal4py as d4p
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  # Twitter API setup
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  def twitter_api_setup():
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- consumer_key = 'YOUR_TWITTER_API_KEY'
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- consumer_secret = 'YOUR_TWITTER_API_SECRET'
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- access_token = 'YOUR_TWITTER_ACCESS_TOKEN'
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- access_token_secret = 'YOUR_TWITTER_ACCESS_SECRET'
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- auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
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- auth.set_access_token(access_token, access_token_secret)
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- api = tweepy.API(auth)
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- return api
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  # YouTube API setup
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  def youtube_api_setup():
@@ -25,13 +20,17 @@ def youtube_api_setup():
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  return youtube
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  # Fetch Twitter sentiment
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- def fetch_twitter_sentiment(symbol, api, sentiment_model):
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- tweets = api.search(q=symbol, lang='en', count=100, tweet_mode='extended')
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- tweet_texts = [tweet.full_text for tweet in tweets]
 
 
 
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  sentiments = sentiment_model(tweet_texts)
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  sentiment_scores = [s['label'] for s in sentiments]
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  positive = sentiment_scores.count('POSITIVE')
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  negative = sentiment_scores.count('NEGATIVE')
 
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  return positive, negative
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  # Fetch YouTube sentiment
@@ -84,8 +83,8 @@ def main():
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  # Twitter Sentiment
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  st.subheader("Twitter Sentiment Analysis")
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- api = twitter_api_setup()
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- positive_twitter, negative_twitter = fetch_twitter_sentiment(stock_symbol, api, sentiment_model)
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  st.write(f"Positive Tweets: {positive_twitter}, Negative Tweets: {negative_twitter}")
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  # YouTube Sentiment
@@ -118,3 +117,4 @@ def main():
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  if __name__ == "__main__":
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  main()
 
 
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  # Twitter API setup
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  def twitter_api_setup():
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+ bearer_token = 'YOUR_TWITTER_BEARER_TOKEN'
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+ client = tweepy.Client(bearer_token=bearer_token)
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+ return client
 
 
 
 
 
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  # YouTube API setup
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  def youtube_api_setup():
 
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  return youtube
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  # Fetch Twitter sentiment
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+ def fetch_twitter_sentiment(symbol, client, sentiment_model):
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+ query = f"{symbol} -is:retweet lang:en"
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+ tweets = client.search_recent_tweets(query=query, max_results=100, tweet_fields=['text'])
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+
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+ tweet_texts = [tweet.text for tweet in tweets.data] if tweets.data else []
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+
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  sentiments = sentiment_model(tweet_texts)
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  sentiment_scores = [s['label'] for s in sentiments]
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  positive = sentiment_scores.count('POSITIVE')
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  negative = sentiment_scores.count('NEGATIVE')
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+
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  return positive, negative
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  # Fetch YouTube sentiment
 
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  # Twitter Sentiment
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  st.subheader("Twitter Sentiment Analysis")
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+ client = twitter_api_setup()
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+ positive_twitter, negative_twitter = fetch_twitter_sentiment(stock_symbol, client, sentiment_model)
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  st.write(f"Positive Tweets: {positive_twitter}, Negative Tweets: {negative_twitter}")
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  # YouTube Sentiment
 
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  if __name__ == "__main__":
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  main()
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+