Semi-supervised sentiment classification with self-training on feature subspaces

Abstract

In sentiment classification, labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for sentiment classification to improve the performance by exploring the knowledge in unlabeled data. In this paper, we analyze the possibility and the difficulty of semisupervised sentiment classification and indicate that noisy features may be the main reason for badly influencing the performance. To overcome this problem, we propose a novel self-training approach where multiple feature subspace-based classifiers are utilized to explore a set of good features for better classification decision and to select the informative samples for automatically labeling. Evaluation over multiple data sets shows the effectiveness of our self-training approach for semi-supervised sentiment classification.