Semi-Stacking for Semi-supervised Sentiment Classification

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. Specifically, we apply meta-learning 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. Evaluation on four domains shows that such a semi-stacking strategy performs consistently better than its member algorithms.