input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques and a classification model. The function should tokenize the reviews, remove stopwords, normalize the words, generate features using CountVectorizer, split the data into training and test sets, train a classification model, and print a classification report.
  assert sentiment_analysis([(1, "I love this product"), (0, "This product is terrible")]) == "              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00         1\n           1       1.00      1.00      1.00         1\n\n    accuracy                           1.00         2\n   macro avg       1.00      1.00      1.00         2\nweighted avg       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
  import nltk
  from nltk.corpus import stopwords
  from nltk.tokenize import word_tokenize
  import string
  
  def sentiment_analysis(reviews):
      nltk.download('punkt')
      nltk.download('stopwords')
      stop_words = set(stopwords.words('english'))
      labels = [label for label, _ in reviews]
      texts = [text for _, text in reviews]
      
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [word for word in tokens if word.isalpha() and word not in stop_words]
          return ' '.join(tokens)
      
      texts = [preprocess(text) for text in texts]
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
      
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
      
      model = MultinomialNB()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
      return report
  