input: 
  def sentiment_analysis(reviews_file: str) -> str:
      """Perform sentiment analysis on product reviews using natural language processing techniques.
      The function tokenizes, filters stopwords, normalizes words, and generates features using 
      CountVectorizer. It then trains a Naive Bayes classifier on the data and predicts sentiment 
      on a test set. Finally, it returns a classification report.
  
      >>> sentiment_analysis('reviews.csv')
      '              precision    recall  f1-score   support\n\n         0.0       0.89      0.92      0.91      1000\n         1.0       0.92      0.89      0.90      1000\n\n    accuracy                           0.90      2000\n   macro avg       0.90      0.90      0.90      2000\nweighted avg       0.90      0.90      0.90      2000\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
      import string
  
      # Load the dataset
      df = pd.read_csv(reviews_file)
  
      # Preprocessing functions
      def preprocess(text):
          text = text.lower()
          tokens = word_tokenize(text)
          tokens = [word for word in tokens if word.isalnum()]
          tokens = [word for word in tokens if word not in stopwords.words('english')]
          lemmatizer = WordNetLemmatizer()
          tokens = [lemmatizer.lemmatize(word) for word in tokens]
          return ' '.join(tokens)
  
      df['review'] = df['review'].apply(preprocess)
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['review'])
      y = df['sentiment']
  
      # Train-test split
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and evaluate
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred, output_dict=False)
  
      return report