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 sentiment classifications for each tweet, where "Positive" indicates a positive sentiment and "Negative" indicates a negative sentiment. If the prediction function returns 1, classify the sentiment as "Positive"; if it returns 0, classify it as "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!", "This is terrible."])==["Positive", "Negative"]
  assert sentiment_analysis(["Great day today!", "I'm not happy."])==["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