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 semi-supervised 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.
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Gao, W., Li, S., Xue, Y., Wang, M., Zhou, G. (2014). Semi-supervised Sentiment Classification with Self-training on Feature Subspaces. In: Su, X., He, T. (eds) Chinese Lexical Semantics. CLSW 2014. Lecture Notes in Computer Science(), vol 8922. Springer, Cham. https://doi.org/10.1007/978-3-319-14331-6_23
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DOI: https://doi.org/10.1007/978-3-319-14331-6_23
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