input: 
  def sentiment_analysis(reviews_file: str) -> str:
      """
      Perform sentiment analysis on product reviews using natural language processing techniques.
      The program tokenizes the reviews, removes stopwords, normalizes the words, and generates
      features using CountVectorizer. It then trains a Naive Bayes model on the training data and
      generates a classification report based on the test data.
      
      Example:
      >>> sentiment_analysis('reviews.csv')
      "              precision    recall  f1-score   support\n\n           0       0.85      0.89      0.87       100\n           1       0.90      0.86      0.88       120\n\n    accuracy                           0.88       220\n   macro avg       0.88      0.88      0.88       220\nmicro avg       0.88      0.88      0.88       220\n"
      """
  
output:     import pandas as pd
      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
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
  
      # Load the reviews data
      reviews_df = pd.read_csv(reviews_file)
  
      # Preprocess the reviews
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
  
      def preprocess_text(text):
          tokens = word_tokenize(text.lower())
          filtered_tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalnum() and word not in stop_words]
          return ' '.join(filtered_tokens)
  
      reviews_df['processed_review'] = reviews_df['review'].apply(preprocess_text)
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(reviews_df['processed_review'], reviews_df['sentiment'], test_size=0.2, random_state=42)
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      # Train the Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train_vec, y_train)
  
      # Predict the sentiment on the test data
      y_pred = model.predict(X_test_vec)
  
      # Generate the classification report
      report = classification_report(y_test, y_pred, output_dict=False)
  
      return report