input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization, stopword removal, lemmatization, and n-grams. The function should use a classification model to assign sentiments to the reviews and return a classification report.
  assert sentiment_analysis("Python code here for the function's body") == "classification_report_output_here"
  assert sentiment_analysis("Another test case here") == "Another classification_report_output_here"
output: def sentiment_analysis(reviews):
      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.isalnum() and word not in stop_words]
          return ' '.join(tokens)
  
      processed_reviews = [preprocess(review) for review in reviews]
  
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(processed_reviews)
  
      y = [1] * len(reviews)  # Dummy labels for demonstration
  
      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)