input: Write a function to train a Naive Bayes classifier for sentiment analysis and evaluate its performance.
  assert round(train_and_evaluate_naive_bayes_classifier('sentiment.csv'), 2) >= 0.70
output: def train_and_evaluate_naive_bayes_classifier(filepath):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv(filepath)
      
      # Split the data into features and labels
      X = data['text']
      y = data['sentiment']
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
      
      # Train the Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train_vec, y_train)
      
      # Predict the labels for the test set
      y_pred = clf.predict(X_test_vec)
      
      # Calculate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy