input: Write a function to perform sentiment analysis on product reviews. The function should use natural language processing techniques like tokenization and n-grams to generate features for sentiment analysis, and use a classification model to assign sentiments to the reviews. The function should read in a CSV file containing reviews and their sentiment labels, preprocess the text by tokenizing and removing stopwords, normalize words, generate features using `CountVectorizer`, split the data into training and test sets, train a `MultinomialNB` classifier, make predictions on the test set, and print a classification report.
  
  
  import numpy as np
  import pandas as pd
  import nltk
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from nltk.stem import WordNetLemmatizer
  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
  
  def sentiment_analysis(csv_file):
      # Read in the data
      data = pd.read_csv(csv_file)
  
      # Tokenize the texts
      data['tokenized'] = data['review'].apply(word_tokenize)
  
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenized'].apply(lambda x: [val for val in x if val not in stop_words])
  
      # Normalize words
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalized'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
  
      # Generate features using CountVectorizer
      cv = CountVectorizer()
      data['features'] = cv.fit_transform(data['normalized'].astype('str')).toarray()
  
      # Split into training and test sets
      X = data['features']
      y = data['sentiment']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train model
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
  
      # Predict using the test set
      predictions = model.predict(X_test)
  
      # Generate a classification report
      report = classification_report(y_test, predictions)
      return report
  
  # Test cases
  assert sentiment_analysis('reviews.csv') == "Expected classification report output"
  
  
  Note: Replace "Expected classification report output" with the actual classification report output that the function should return.
output: def sentiment_analysis(csv_file):
      # Read in the data
      data = pd.read_csv(csv_file)
  
      # Tokenize the texts
      data['tokenized'] = data['review'].apply(word_tokenize)
  
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenized'].apply(lambda x: [val for val in x if val not in stop_words])
  
      # Normalize words
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalized'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
  
      # Generate features using CountVectorizer
      cv = CountVectorizer()
      data['features'] = cv.fit_transform(data['normalized'].astype('str')).toarray()
  
      # Split into training and test sets
      X = data['features']
      y = data['sentiment']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train model
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
  
      # Predict using the test set
      predictions = model.predict(X_test)
  
      # Generate a classification report
      report = classification_report(y_test, predictions)
      return report