input: Write a function to classify positive and negative sentiment in tweets. It should first clean each tweet using a preprocessing function, then vectorize the cleaned tweet, and finally predict its sentiment using a predict_sentiment function. If the predicted sentiment is 1, classify it as "Positive"; if it is 0, classify it 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 product!', 'I hate slow internet.'])==['Positive', 'Negative']
  assert sentiment_analysis(['Feeling great today.', 'Not happy at all.'])==['Positive', 'Negative']
  assert sentiment_analysis(['This is amazing.', 'Worst experience ever.'])==['Positive', 'Negative']
  assert sentiment_analysis([])==[]
  assert sentiment_analysis(['Balance is key to life.'])==['Positive']
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