input: Write a function to classify positive and negative sentiment in a list of tweets. The function should return a list of sentiment labels ('Positive' or 'Negative') for each tweet. Use preprocessing, vectorization, and a prediction model to determine the sentiment of each tweet. If the tweet is predicted as 1, label it as 'Positive', and if it is predicted as 0, label it 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 movie!", "This is terrible."]) == ["Positive", "Negative"]
  assert sentiment_analysis(["Happy day", "Sad day"]) == ["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