input: Write a function to classify positive and negative sentiment in tweets. The function should iterate through each tweet, preprocess it, vectorize it, and predict the sentiment. If the prediction is 1, it should append "Positive" to the result list; if the prediction is 0, it should append "Negative" to the result list. If the input list of tweets is empty, it should return an empty list.
  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!", "I hate this."])==["Positive", "Negative"]
  assert sentiment_analysis(["This is amazing!", "This is terrible."])==["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