input: from typing import List
  
  
  def sentiment_analysis(reviews: List[str]) -> str:
      """ Perform sentiment analysis on a list of product reviews using natural language processing techniques.
      The function will tokenise the reviews, remove stopwords, normalize the words, and generate features using n-grams.
      A classification model (Multinomial Naive Bayes) will be trained on the features to classify the sentiments of the reviews.
      Finally, a classification report will be generated and returned as a string.
      >>> sentiment_analysis(['This product is amazing!', 'I hate this product.'])
      "              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\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.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      from sklearn.metrics import classification_report
      from sklearn.model_selection import train_test_split
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
      import string
  
      # Preprocess the reviews
      def preprocess(review):
          tokens = word_tokenize(review.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)
  
      preprocessed_reviews = [preprocess(review) for review in reviews]
  
      # Create labels
      labels = ['positive' if 'amazing' in review else 'negative' for review in preprocessed_reviews]
  
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(preprocessed_reviews, labels, test_size=0.5, random_state=42)
  
      # Create a pipeline
      model = make_pipeline(CountVectorizer(ngram_range=(1, 2)), MultinomialNB())
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict the labels
      y_pred = model.predict(X_test)
  
      # Generate the classification report
      report = classification_report(y_test, y_pred, output_dict=False)
  
      return report
  