input: 
  def sentiment_analysis(data_path: str) -> str:
      """Performs sentiment analysis on product reviews using natural language processing techniques.
      It tokenizes the text, removes stopwords, normalizes words, and uses a CountVectorizer to generate features.
      The model used for classification is Multinomial Naive Bayes. The function returns a classification report.
      
      >>> sentiment_analysis('reviews.csv')
      """
  
output:     import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import classification_report
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
      import re
  
      # Load the dataset
      df = pd.read_csv(data_path)
  
      # Preprocessing
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
  
      def preprocess_text(text):
          text = re.sub(r'\W', ' ', text)  # Remove non-word characters
          text = re.sub(r'\s+', ' ', text)  # Remove extra spaces
          text = text.lower()  # Convert to lowercase
          tokens = word_tokenize(text)  # Tokenize
          tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalpha() and word not in stop_words]  # Lemmatize and remove stopwords
          return ' '.join(tokens)
  
      df['processed_text'] = df['review'].apply(preprocess_text)
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['processed_text'])
      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)
  
      # Model training
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Prediction
      y_pred = model.predict(X_test)
  
      # Classification report
      report = classification_report(y_test, y_pred)
      return report