input: Write a function to classify positive and negative sentiment in tweets. The function should clean each tweet, vectorize it, and then predict its sentiment. If the prediction is 1, classify the sentiment as "Positive"; if the prediction is 0, classify the sentiment as "Negative".
  
  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 product!", "I hate shopping"])==["Positive", "Negative"]
  assert sentiment_analysis(["What a great day!", "Worst experience ever"])==["Positive", "Negative"]
  assert sentiment_analysis(["The movie was fantastic", "The service was terrible"])==["Positive", "Negative"]
  assert sentiment_analysis([])==[]
  assert sentiment_analysis(["I am neutral"])==["Negative"]
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