Therefore, we use a semi-supervised method with a small amount of labeled data and a large amount of unlabeled data, because to prepare a large quantity of labeled data is costly, while unlabeled data is easy to obtain.
we adopt the Naïve Bayes classifier backed up with the Expectation Maximization (EM) algorithm (Dempster et al., 1977) for semi-supervised learning.