input: def analyze_sentiment(filepath: str) -> str:
      """ Perform sentiment analysis on product reviews using natural language processing techniques.
      The program will tokenize the reviews, remove stopwords, normalize words using lemmatization,
      generate features using CountVectorizer, split the data into training and test sets, train a 
      Multinomial Naive Bayes model, and return a classification report.
      
      >>> analyze_sentiment('reviews.csv')
      '               precision    recall  f1-score   support\n\n           0       0.88      0.77      0.82        50\n           1       0.81      0.90      0.85        63\n\n    accuracy                           0.84       113\n   macro avg       0.85      0.84      0.84       113\nweighted avg       0.84      0.84      0.84       113\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(filepath)
  
      # Preprocess the text data
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
  
      def preprocess_text(text):
          tokens = word_tokenize(text.lower())
          tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalpha() and word not in stop_words]
          return ' '.join(tokens)
  
      df['processed_reviews'] = df['reviews'].apply(preprocess_text)
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['processed_reviews'])
      y = df['sentiment']
  
      # Split the data into training and test sets
      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 the model
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report