input: Write a python function to build and evaluate a text sentiment classification model using Naive Bayes and TfidfVectorizer.
  assert evaluate_model(data)['accuracy'] > 0.7
  assert evaluate_model(data)['accuracy'] <= 1.0
output: def evaluate_model(data):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      texts, labels = zip(*data)
      texts = list(texts)
      labels = list(labels)
  
      X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2, random_state=42)
  
      model = make_pipeline(TfidfVectorizer(), MultinomialNB())
      model.fit(X_train, y_train)
  
      predictions = model.predict(X_test)
      accuracy = accuracy_score(y_test, predictions)
  
      return {'accuracy': accuracy}