input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams, and use a classification model to assign sentiments to the reviews. The function should read the data from a CSV file, tokenize the reviews, remove stopwords, normalize the words, generate features using CountVectorizer, split the data into training and test sets, train a Multinomial Naive Bayes model, and print a classification report.
  assert sentiment_analysis_fn()
output: def sentiment_analysis_fn():
      import pandas as pd
      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
      import string
  
      def preprocess_text(text):
          tokens = word_tokenize(text.lower())
          tokens = [word for word in tokens if word.isalnum()]
          tokens = [word for word in tokens if word not in stopwords.words('english')]
          return ' '.join(tokens)
  
      data = pd.read_csv('reviews.csv')
      data['review'] = data['review'].apply(preprocess_text)
  
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(data['review'])
      y = data['sentiment']
  
      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)
      print(classification_report(y_test, y_pred))