input: def sentiment_analysis(reviews_csv_path: str) -> str:
      """ Perform sentiment analysis on product reviews using natural language processing techniques.
      The program reads reviews from a CSV file, tokenizes the texts, removes stopwords, normalizes 
      the words, generates features using CountVectorizer, splits the data into training and test sets,
      trains a Naive Bayes classifier, predicts sentiments on the test set, and finally prints a 
      classification report.
      >>> sentiment_analysis('reviews.csv')
      (Outputs a classification report for the sentiment analysis on the product reviews)
      """
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 reviews data
      reviews_df = pd.read_csv(reviews_csv_path)
  
      # Preprocessing functions
      def preprocess_text(text):
          tokens = word_tokenize(text.lower())
          tokens = [word for word in tokens if word.isalpha()]
          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)
  
      # Apply preprocessing to the reviews
      reviews_df['processed_review'] = reviews_df['review'].apply(preprocess_text)
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(reviews_df['processed_review'])
      y = reviews_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 Naive Bayes classifier
      classifier = MultinomialNB()
      classifier.fit(X_train, y_train)
  
      # Predict sentiments on the test set
      y_pred = classifier.predict(X_test)
  
      # Print the classification report
      report = classification_report(y_test, y_pred)
      print(report)
      return report