input: 
  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_file: str) -> str:
      """
      Perform sentiment analysis on product reviews using natural language processing techniques.
      The program tokenizes the texts, removes stopwords, normalizes words, generates features 
      using CountVectorizer, and uses a Multinomial Naive Bayes classification model to assign 
      sentiments to the reviews. The classification report is then printed.
      
      >>> sentiment_analysis('reviews.csv')
      '              precision    recall  f1-score   support\\n\\n         0       0.85      0.89      0.87       100\\n         1       0.90      0.86      0.88       100\\n\\n    accuracy                           0.88       200\\n   macro avg       0.88      0.88      0.88       200\\nweighted avg       0.88      0.88      0.88       200\\n'
      """
      
      # Read in the data
      data = pd.read_csv(data_file)
  
      # 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
  
output:     # Print the classification report
      print(report)