Semi-Supervised Sentiment Classification with a Ensemble Strategy
Shoushan Li · Zhongwen xinxi xuebao · 2013
Sentiment classification aims to predict the sentimental orientation expressed in the text.In this paper,we investigate the semi-supervised approaches for sentiment classification in a ensemble learning framework where a abound of unlabeled data is leveraged to enhance the classification performance together with a small amount of labeled data.To improve the performance of the semi-supervised learning approach,we propose a novel ensemble method based on label consistency.Specifically,we combine two popular semi-supervised methods: co-training with random feature subspaces and label propagation to generate the pseudo labeled data for updating the initial labeled data.First,the unlabeled data are labeled by the two semi-supervised learning approaches separately.Then,the unlabeled samples with the consistent labels are considered as pseudo labeled data.Finally,the labeled data is updated with the pseudo labeled data.Experimental study shows that our approach is capable of effectively reducing the error of the pseudo labeled data and thus achieves much better performances than some other approaches for semi-supervised sentiment classification.