input: Write a function to classify positive and negative sentiment in tweets. If a tweet is predicted as positive, it should be denoted as "Positive", otherwise, denoted as "Negative".
  def sentiment_analysis(tweets):
      results = [] 
      for tweet in tweets:
          tweet = preprocessing(tweet)
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
      return results
  assert sentiment_analysis(["I love sunny days!", "This weather is horrible."])==["Positive", "Negative"]
  assert sentiment_analysis(["Feeling great today.", "Can't stand the cold."])==["Positive", "Negative"]
  assert sentiment_analysis(["The movie was fantastic!", "Worst service ever!"])==["Positive", "Negative"]
  assert sentiment_analysis([])==[]
output: def sentiment_analysis(tweets):
      results = [] 
      for tweet in tweets:
          tweet = preprocessing(tweet)
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
      return results