input: 
  def sentiment_analysis(tweets):
      """
      Classify positive and negative sentiment in tweets.
      The function processes each tweet by cleaning it, vectorizing it, and predicting its sentiment.
      If the prediction is 1, the sentiment is classified as "Positive".
      If the prediction is 0, the sentiment is classified as "Negative".
      The function returns a list of sentiment classifications for each tweet.
      
      Example:
      sentiment_analysis(["I'm happy today!", "I feel sad."])
      >>> ['Positive', 'Negative']
      """
      # 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
  
output:     # 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