input: 
  def sentiment_analysis(data: pd.DataFrame, alpha: float) -> str:
      """Perform sentiment analysis on product reviews using natural language processing techniques.
      The function tokenizes the reviews, removes stopwords, normalizes the words, generates features
      using CountVectorizer, splits the data into training and test sets, trains a Naive Bayes model,
      and returns a classification report.
      >>> sentiment_analysis(pd.DataFrame({
      ...     'review': ['This product is amazing!', 'Terrible purchase! Absolutely disappointed!'],
      ...     'sentiment': ['Positive', 'Negative']
      ... }), 0.1)
      '              precision    recall  f1-score   support\\n\\nNegative       1.00      1.00      1.00         1\\nPositive       1.00      1.00      1.00         1\\n\\navg / total       1.00      1.00      1.00         2\\n'
      """
  
output:     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 re
  
      # Tokenize and preprocess the reviews
      def preprocess(text):
          text = re.sub(r'\W', ' ', text)  # Remove non-word characters
          text = re.sub(r'\s+', ' ', text)  # Remove extra spaces
          tokens = word_tokenize(text.lower())  # Tokenize and convert to lowercase
          tokens = [word for word in tokens if word not in stopwords.words('english')]  # Remove stopwords
          lemmatizer = WordNetLemmatizer()
          tokens = [lemmatizer.lemmatize(word) for word in tokens]  # Lemmatize words
          return ' '.join(tokens)
  
      data['review'] = data['review'].apply(preprocess)
  
      # Generate features using CountVectorizer
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data['review'])
      y = data['sentiment']
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=alpha, random_state=42)
  
      # Train a Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and generate classification report
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report