input: 
  import numpy as np
  import pandas as pd
  import nltk
  from nltk.tokenize import word_tokenize
  import matplotlib.pyplot as plt
  from nltk.corpus import stopwords
  from nltk.stem import WordNetLemmatizer 
  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
  
  def sentiment_analysis(reviews: pd.DataFrame) -> str:
      """ Perform sentiment analysis on product reviews using natural language processing techniques. The program tokenizes the texts, removes stopwords, normalizes words, generates features using CountVectorizer, and trains a Naive Bayes classifier. Finally, it generates and returns a classification report.
      >>> sample_data = pd.DataFrame({
              'review': ['This product is amazing!', 'Very disappointed with the quality.'],
              'sentiment': ['positive', 'negative']
          })
      >>> print(sentiment_analysis(sample_data))
                    precision    recall  f1-score   support
  
          negative       1.00      1.00      1.00         1
          positive       1.00      1.00      1.00         1
  
          accuracy                           1.00         2
          macro avg       1.00      1.00      1.00         2
          weighted avg    1.00      1.00      1.00         2
      """
      
      # Tokenise the texts
      reviews['tokenised'] = reviews['review'].apply(word_tokenize)
      
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      reviews['filtered'] = reviews['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
      
      # Normalize words
      wordnet_lemmatizer = WordNetLemmatizer()
      reviews['normalised'] = reviews['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
      
      # Generate features using CountVectorizer
      cv = CountVectorizer()
      reviews['features'] = cv.fit_transform(reviews['normalised'].astype('str')).toarray()
      
      # Split into training and test sets
      X = reviews['features']
      y = reviews['sentiment']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Train model
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
      
      # Predict using the test set
      predictions = model.predict(X_test)
      
      # Generate a classification report
      report = classification_report(y_test, predictions)
      return report
  
output:     return report