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 sentiments as "Positive" or "Negative". For each tweet, perform preprocessing and vectorization, then use a prediction function to determine the sentiment.
  
  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 coding!", "This is really frustrating"])==["Positive", "Negative"]
  assert sentiment_analysis(["Great day", "I hate waiting"])==["Positive", "Negative"]
  assert sentiment_analysis(["Happy birthday!", "Super angry right now"])==["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