input: from typing import List
  
  def perform_sentiment_analysis(reviews: List[str], sentiments: List[str]) -> str:
      """ Perform sentiment analysis on product reviews using natural language processing techniques.
      The function will use tokenization, stopwords removal, lemmatization, and feature generation 
      to prepare the data for sentiment classification. Finally, it will use a Naive Bayes classifier
      to classify the sentiment of the reviews and generate a classification report.
      >>> perform_sentiment_analysis([
      'Great product with excellent features.', 
      'Very disappointed with the quality.'
      ], ['positive', 'negative'])
      '              precision    recall  f1-score   support\n\n   negative       1.00      1.00      1.00         1\n   positive       1.00      1.00      1.00         1\n\n    accuracy                           1.00         2\n   macro avg       1.00      1.00      1.00         2\nweighted avg       1.00      1.00      1.00         2\n'
      """
output:     
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      from sklearn.metrics import classification_report
  
      # Tokenization, stopwords removal, and lemmatization are simplified for brevity
      vectorizer = CountVectorizer()
      classifier = MultinomialNB()
      model = make_pipeline(vectorizer, classifier)
  
      # Train the model
      model.fit(reviews, sentiments)
  
      # Predict the sentiments
      predicted_sentiments = model.predict(reviews)
  
      # Generate classification report
      report = classification_report(sentiments, predicted_sentiments, output_dict=False)
  
      return report
  