def update(self, new_positive_data=None,<EOL>new_unlabeled_data=None, positive_prob_prior=<NUM_LIT:0.5>,<EOL>*args, **kwargs):
self.positive_prob_prior = positive_prob_prior<EOL>if new_positive_data:<EOL><INDENT>self.positive_set += new_positive_data<EOL>self.positive_features += [self.extract_features(d)<EOL>for d in new_positive_data]<EOL><DEDENT>if new_unlabeled_data:<EOL><INDENT>self.unlabeled_set += new_unlabeled_data<EOL>self.unlabeled_features += [self.extract_features(d)<EOL>for d in new_unlabeled_data]<EOL><DEDENT>self.classifier = self.nltk_class.train(self.positive_features,<EOL>self.unlabeled_features,<EOL>self.positive_prob_prior,<EOL>*args, **kwargs)<EOL>return True<EOL>
Update the classifier with new data and re-trains the
        classifier.

        :param new_positive_data: List of new, labeled strings.
        :param new_unlabeled_data: List of new, unlabeled strings.