In this paper, we address semi-supervised sentiment learning via semi-stacking, which integrates two or more semi-supervised learning algorithms from an ensemble learning perspective.
we apply metalearning to predict the unlabeled data given the outputs from the member algorithms and propose N-fold cross validation to guarantee a suitable size of the data for training the meta-classifier.