input: from typing import List
  
  def sentiment_analysis(data: pd.DataFrame, alpha: float) -> str:
      """ Perform sentiment analysis on product reviews using natural language processing techniques.
      Tokenize the reviews, remove stopwords, normalize words, and generate features using CountVectorizer.
      Train a Multinomial Naive Bayes classifier and generate a classification report.
      >>> sentiment_analysis(pd.DataFrame({'review': ['This product is great!', 'This product is awful.'], 'sentiment': ['Positive', 'Negative']}), 0.1)
      '              precision    recall  f1-score   support\\n\\n     Negative       1.00      1.00      1.00         1\\n     Positive       1.00      1.00      1.00         1\\n\\navg / total       1.00      1.00      1.00         2\\n'
      """
      return report
output:     
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import Pipeline
      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
      import string
  
      def preprocess_text(text):
          tokens = word_tokenize(text.lower())
          tokens = [word for word in tokens if word.isalnum() and word not in stopwords.words('english')]
          return ' '.join(tokens)
  
      data['review'] = data['review'].apply(preprocess_text)
      X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=alpha, random_state=42)
  
      pipeline = Pipeline([
          ('vectorizer', CountVectorizer()),
          ('classifier', MultinomialNB())
      ])
  
      pipeline.fit(X_train, y_train)
      y_pred = pipeline.predict(X_test)
      report = classification_report(y_test, y_pred)
      return report
  