input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams to generate features for sentiment analysis and use a classification model to assign sentiments to the reviews.
  assert perform_sentiment_analysis("This product is amazing! Highly recommended.") == "Positive"
  assert perform_sentiment_analysis("It's just okay, nothing special.") == "Neutral"
  assert perform_sentiment_analysis("I am extremely disappointed with the quality.") == "Negative"
output: def perform_sentiment_analysis(review):
      positive_words = {"amazing", "highly recommended", "excellent", "great", "wonderful"}
      negative_words = {"disappointed", "terrible", "awful", "poor", "bad"}
      
      words = review.lower().split()
      positive_count = sum(1 for word in words if word in positive_words)
      negative_count = sum(1 for word in words if word in negative_words)
      
      if positive_count > negative_count:
          return "Positive"
      elif negative_count > positive_count:
          return "Negative"
      else:
          return "Neutral"