input: def perform_sentiment_analysis(dataset_path: str):
      """ Perform sentiment analysis on product reviews using natural language processing techniques.
      Tokenize the text, remove stopwords, normalize words, generate features using CountVectorizer,
      split into training and test sets, train a Naive Bayes model, and generate a classification report.
      >>> perform_sentiment_analysis('reviews.csv')
      precision    recall  f1-score   support
      ...
      """
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 dataset
      df = pd.read_csv(dataset_path)
  
      # Preprocessing
      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_text'] = df['review'].apply(preprocess_text)
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['processed_text'])
      y = df['sentiment']
  
      # Split 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 Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and generate classification report
      y_pred = model.predict(X_test)
      print(classification_report(y_test, y_pred))
  