input: Write a function to classify positive and negative sentiment in tweets. If the prediction for a given tweet is 1, classify it as "Positive"; otherwise, classify it as "Negative". The function should iterate through each tweet in the input list, preprocess it, vectorize it, and then predict the sentiment. The final output should be a list of sentiment classifications for each tweet in the input list. The function should be named sentiment_analysis.
  
  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!", "I hate waiting in line"])==["Positive", "Negative"]
  assert sentiment_analysis(["Today is a great day", "This is so frustrating"])==["Positive", "Negative"]
  assert sentiment_analysis([])==[]
  assert sentiment_analysis(["I feel neutral"])==["Negative"] # Assuming predict_sentiment returns 0 for neutral
  assert sentiment_analysis(["Best day ever!", "Worst experience of my life"])==["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")
          else:
              results.append("Negative")
      return results