5_twitter.py

# ANALYSIS
def twitter_analysis(twitter_api, stock):
  sentiment = {}

  tweets = tweepy.Cursor(twitter_api.search, q=stock, lang='en').items(TWITTER_TWEETS_COUNT)

  negative = []
  positive = []
  neutral = []

  for tweet in tweets:
    score = SentimentIntensityAnalyzer().polarity_scores(tweet.text)
    compound = score['compound']
    if compound >= 0.05:
      positive.append(tweet)
    elif compound <= -0.05:
      negative.append(tweet)
    elif compound > -0.05 and compound < 0.05:
      neutral.append(tweet)

  if len(positive) > len(negative):
    sentiment_str = 'POSITIVE'
  elif len(negative) > len(positive):
    sentiment_str = 'NEGATIVE'
  else:
    sentiment_str = 'NA'

  sentiment = {'sentiment': sentiment_str, 'stock' : stock, \
                    'positive': len(positive), \
                    'negative': len(negative), \
                    'neutral': len(neutral)}

  return sentiment