input: 
  def sentiment_analysis(tweets: List[str]) -> List[str]:
      # 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 movie!", "This is so bad."])
  ['Positive', 'Negative']
  """
  
output:     # Define the preprocessing function
      def preprocessing(text: str) -> str:
          import re
          text = re.sub(r'[^\w\s]', '', text)  # Remove punctuation
          text = text.lower()  # Convert to lowercase
          return text
  
      # Define the vectorize function
      def vectorize(text: str) -> List[float]:
          from sklearn.feature_extraction.text import CountVectorizer
          vectorizer = CountVectorizer()
          vectorizer.fit([text])
          return vectorizer.transform([text]).toarray()[0]
  
      # Define the predict_sentiment function
      def predict_sentiment(vector: List[float]) -> int:
          # Dummy model for demonstration purposes
          if sum(vector) > 0:
              return 1  # Positive
          else:
              return 0  # Negative