input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques. The function should tokenize the reviews, remove stopwords, normalize words, generate features using CountVectorizer, and use a Naive Bayes classifier to predict sentiments. It should return the classification report for the test set.
  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(data):
      # Tokenise the texts
      data['tokenised'] = data['review'].apply(word_tokenize)
      
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
      
      # Normalize words
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalised'] = 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['normalised'].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
  
  assert sentiment_analysis(pd.read_csv('reviews.csv')) is not None
  assert sentiment_analysis(pd.read_csv('reviews.csv')).split('\n')[0].startswith('              precision    recall  f1-score   support')
output: def sentiment_analysis(data):
      data['tokenised'] = data['review'].apply(word_tokenize)
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalised'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
      cv = CountVectorizer()
      data['features'] = cv.fit_transform(data['normalised'].astype('str')).toarray()
      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)
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      report = classification_report(y_test, predictions)
      return report