input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams. The function should read in a CSV file containing reviews, tokenize the texts, 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("reviews.csv")  # This assert statement assumes that the function will run without errors and print the classification report.
output: 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
  import nltk
  from nltk.corpus import stopwords
  from nltk.tokenize import word_tokenize
  import string
  
  def sentiment_analysis(csv_file):
      nltk.download('punkt')
      nltk.download('stopwords')
      
      df = pd.read_csv(csv_file)
      stop_words = set(stopwords.words('english'))
      
      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)
      
      df['processed_review'] = df['review'].apply(preprocess)
      
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(df['processed_review'])
      y = df['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))