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_csv_path: str) -> str:
      """Perform sentiment analysis on product reviews using NLP techniques and a Naive Bayes classifier.
      The program reads review data from a CSV file, processes the text (tokenization, stopword removal, lemmatization),
      generates features using CountVectorizer, trains a Naive Bayes classifier, and returns a classification report.
      
      Example:
      >>> sentiment_analysis('reviews.csv')
      '              precision    recall  f1-score   support\\n\\n         0       0.90      0.95      0.92       100\\n         1       0.93      0.87      0.90        80\\n\\n    accuracy                           0.91       180\\n   macro avg       0.91      0.91      0.91       180\\nweighted avg       0.91      0.91      0.91       180\\n'
      """
      
      # Read in the data
      data = pd.read_csv(reviews_csv_path)
  
      # Tokenize the texts
      data['tokenised'] = data['review'].apply(word_tokenize)
  
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
  
      # Normalize words
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalised'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
  
      # Generate features using CountVectorizer
      cv = CountVectorizer()
      data['features'] = cv.fit_transform(data['normalised'].astype('str')).toarray()
  
      # Split into training and test sets
      X = data['features']
      y = data['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:     # Visualize the results
      plt.figure(figsize=(10, 5))
      plt.subplot(1, 2, 1)
      plt.hist(y_test, bins=2, alpha=0.5, label='Actual', color='blue')
      plt.hist(predictions, bins=2, alpha=0.5, label='Predicted', color='red')
      plt.legend(loc='upper right')
      plt.title('Actual vs Predicted Sentiment')
  
      plt.subplot(1, 2, 2)
      plt.bar(['Negative', 'Positive'], [len(y_test[y_test == 0]), len(y_test[y_test == 1])], color='blue', alpha=0.5)
      plt.bar(['Negative', 'Positive'], [len(predictions[predictions == 0]), len(predictions[predictions == 1])], color='red', alpha=0.5)
      plt.title('Sentiment Distribution')
      plt.show()