input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques. The function should tokenize the reviews, remove stopwords, normalize words, generate features using CountVectorizer, and train a classification model to assign sentiments to the reviews. Return the classification report as output.
  
  assert perform_sentiment_analysis(['This product is amazing!', 'Terrible experience with this product.']) == 'classification report output'
  assert perform_sentiment_analysis(['Excellent quality, highly recommended.', 'The worst product I have ever purchased.']) == 'classification report output'
  assert perform_sentiment_analysis([]) == 'No reviews provided'
output: def perform_sentiment_analysis(reviews):
      if not reviews:
          return 'No reviews provided'
      
      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
      
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
      
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalpha() and word not in stop_words]
          return ' '.join(tokens)
      
      processed_reviews = [preprocess(review) for review in reviews]
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(processed_reviews)
      
      y = ['positive' if '!' in review else 'negative' for review in reviews]
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = MultinomialNB()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      
      return classification_report(y_test, y_pred)