input: Write a function to classify positive and negative sentiment in tweets. The function takes a list of tweets as input and returns a list of sentiment labels ("Positive" or "Negative") corresponding to each tweet. If a tweet has positive sentiment, label it as "Positive"; otherwise, label it as "Negative". Use predefined functions like preprocessing to clean the tweet, vectorize to turn the tweet into a numerical representation, and predict_sentiment to determine the sentiment from the vectorized form.
  
  def sentiment_analysis(tweets):
      # Initialize a list to store the result 
      results = [] 
  
      # Iterate through each tweet
      for tweet in tweets:
          # Clean the tweet 
          tweet = preprocessing(tweet)
  
          # Vectorize the tweet and obtain the prediction 
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
  
          # Append the prediction to the result list
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
  
      return results
  
  assert sentiment_analysis(["I love this!", "This is terrible!"]) == ["Positive", "Negative"]
  assert sentiment_analysis(["Great job!", "I feel awful."]) == ["Positive", "Negative"]
  assert sentiment_analysis(["Happy times!", "Sad times."]) == ["Positive", "Negative"]
  assert sentiment_analysis(["The weather is nice.", "The weather is bad."]) == ["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