input: def perform_sentiment_analysis(data: pd.DataFrame) -> str:
      """
      Perform sentiment analysis on product reviews using natural language processing techniques.
      The program uses tokenization, stopword removal, lemmatization, and n-gram generation to create
      features for sentiment analysis. A Naive Bayes classifier is then trained and tested on the data.
      The classification report is printed, showing the performance of the model.
      
      >>> data = pd.read_csv('reviews.csv')
      >>> perform_sentiment_analysis(data)
      '              precision    recall  f1-score   support\\n\\n         0       0.89      0.92      0.90       100\\n         1       0.92      0.89      0.90       100\\n\\n    accuracy                           0.90       200\\n   macro avg       0.90      0.90      0.90       200\\nweighted avg       0.90      0.90      0.90       200\\n'
      """
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      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
  
      # Preprocessing
      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)
  
      data['processed_review'] = data['review'].apply(preprocess_text)
  
      # Feature Extraction
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(data['processed_review'])
      y = data['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)
  
      # Model Evaluation
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred, output_dict=True)
      report_df = pd.DataFrame(report).transpose()
      return report_df.to_string()
  