input: Write a function to classify positive and negative sentiment in tweets. The function should take a list of tweets as input and return a list of classifications where "Positive" represents a positive tweet and "Negative" represents a negative tweet. The function should perform tweet preprocessing, vectorization, and sentiment prediction. If the prediction is 1, classify the tweet as "Positive"; if 0, classify as "Negative".
  
  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
  
  assert sentiment_analysis(["I love this movie!", "This is terrible"]) == ["Positive", "Negative"]
  assert sentiment_analysis(["I really enjoyed the concert", "I hate traffic jams"]) == ["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