input: Write a function to classify positive and negative sentiment in a list of tweets. The function should iterate through each tweet, preprocess it, vectorize it, and then predict the sentiment. If the prediction is positive, append "Positive" to the results list, otherwise append "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(["Great day!", "Bad weather", "Could be better"])==["Positive", "Negative", "Negative"]
  assert sentiment_analysis([])==[]
  assert sentiment_analysis(["Absolutely fantastic!", "Absolutely awful!"])==["Positive", "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