Semi-supervised Sentiment Classification with Self-training on Feature Subspaces

摘要：

　　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.

关键词： Sentiment Classification Semi-supervised Learning Self-training

作者: Wei Gao Shoushan Li Yunxia Xue Meng Wang Guodong Zhou

作者单位: Natural Language Processing laboratory,School of Computer Science and Technology,Soochow University, School of Humanities,Jiangnan University,Wuxi,China

会议类型: 国际会议

会议名称: Chinese Lexical Semantics 15th Workshop(CLSW 2014)(第十五届汉语词汇语义学国际研讨会)

会议地点: 澳门

会议语种:英文

页码: 231-239

在线出版日期: 2014-06-09（万方平台首次上网日期，不代表论文的发表时间）