input: 
  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
  # Example:
  # sentiment_analysis(['I love this!', 'I hate this.']) == ['Positive', 'Negative']
  
output:     # Define the preprocessing function
      def preprocessing(tweet):
          # Remove punctuation and convert to lowercase
          tweet = ''.join(c for c in tweet if c.isalnum() or c.isspace()).lower()
          return tweet
  
      # Define the vectorize function
      def vectorize(tweet):
          # Dummy vectorization, returns a list of word counts
          words = tweet.split()
          vector = [words.count(word) for word in set(words)]
          return vector
  
      # Define the predict_sentiment function
      def predict_sentiment(vector):
          # Dummy prediction, returns 1 if sum of vector is positive, else 0
          return 1 if sum(vector) > 0 else 0