input: 
  def perform_sentiment_analysis() -> None:
      """Perform sentiment analysis on product reviews using natural language processing techniques.
      The program uses tokenization, n-grams, and a classification model to assign sentiments to reviews.
      Example:
      >>> perform_sentiment_analysis()
                    precision    recall  f1-score   support
  
                 0       0.89      0.92      0.90       100
                 1       0.93      0.91      0.92       150
  
          accuracy                           0.91       250
         macro avg       0.91      0.91      0.91       250
      weighted avg       0.91      0.91      0.91       250
      """
  
output:     import nltk
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      # Sample data
      reviews = [
          "Great product, highly recommended!",
          "Terrible experience, would not buy again.",
          "Good value for the price.",
          "Very disappointed with the quality.",
          "Excellent service and fast delivery."
      ]
      sentiments = [1, 0, 1, 0, 1]
  
      # Tokenization and n-grams
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(reviews)
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
  
      # Train a Naive Bayes classifier
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict sentiments on the test set
      y_pred = model.predict(X_test)
  
      # Print classification report
      print(classification_report(y_test, y_pred))