input: 
  def sentiment_analysis(tweets):
      """Classify positive and negative sentiment in a list of tweets.
      >>> sentiment_analysis(['I love this!', 'This is terrible.', 'Awesome day.'])
      ['Positive', 'Negative', 'Positive']
      >>> sentiment_analysis(['Great news!', 'Very bad experience.', 'Excited for the weekend.'])
      ['Positive', 'Negative', 'Positive']
      """
      # 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
  
output:     # Define the preprocessing function
      def preprocessing(tweet):
          import re
          # Remove special characters and convert to lowercase
          tweet = re.sub(r'\W', ' ', tweet)
          tweet = tweet.lower()
          return tweet
  
      # Define the vectorize function
      def vectorize(tweet):
          from sklearn.feature_extraction.text import CountVectorizer
          vectorizer = CountVectorizer()
          # Fit and transform the tweet
          tweet_vector = vectorizer.fit_transform([tweet])
          return tweet_vector
  
      # Define the predict_sentiment function
      def predict_sentiment(tweet_vector):
          from sklearn.naive_bayes import MultinomialNB
          # Train a Naive Bayes classifier
          clf = MultinomialNB()
          # Dummy training data
          X_train = [[0, 1, 1], [1, 0, 0]]
          y_train = [1, 0]
          clf.fit(X_train, y_train)
          # Predict sentiment
          prediction = clf.predict(tweet_vector.toarray())
          return prediction[0]