input: from typing import List
  
  def sentiment_analysis_model(data_file: str) -> str:
      """ Build and test a sentiment analysis model using Naive Bayes classification.
      The function reads a CSV file containing product reviews, tokenizes the texts,
      removes stopwords, normalizes words using lemmatization, generates features using
      CountVectorizer, splits the data into training and test sets, trains a Multinomial
      Naive Bayes model, and finally generates a classification report.
      >>> sentiment_analysis_model('reviews.csv')
      '              precision    recall  f1-score   support\\n\\n    negative       0.85      0.89      0.87       100\\n    positive       0.92      0.88      0.90       150\\n\\n    accuracy                           0.89       250\\n   macro avg       0.88      0.88      0.88       250\\nweighted avg       0.89      0.89      0.89       250\\n\\n'
      """
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
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
      import string
  
      # Load the data
      df = pd.read_csv(data_file)
  
      # Preprocess the text data
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
  
      def preprocess_text(text):
          tokens = word_tokenize(text.lower())
          tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalpha() and word not in stop_words]
          return ' '.join(tokens)
  
      df['processed_text'] = df['text'].apply(preprocess_text)
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['processed_text'])
      y = df['sentiment']
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and generate classification report
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report
  