Title of article :
An Empirical Study of Unsupervised Sentiment Classification of Chinese Reviews
Author/Authors :
ZHAI, Zhongwu Tsinghua University - Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory of Intelligent Technology and Systems, China , XU, Hua Tsinghua University - Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory of Intelligent Technology and Systems, China , JIA, Peifa Tsinghua University - Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory of Intelligent Technology and Systems, China
Abstract :
This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited existing sentiment resources in Chinese. On the one hand, all available Chinese sentiment lexicons — individual and combined — are evaluated under our proposed framework. On the other hand, the domain dependent sentiment noise words are identified and removed using unlabeled data, to improve the classification performance. To the best of our knowledge, this is the first such attempt. Experiments have been conducted on three open datasets in two domains, and the results show that the proposed algorithm for sentiment noise words removal can improve the classification performance significantly.
Keywords :
sentiment noise words , unsupervised sentiment classification , domain dependent
Journal title :
Tsinghua Science and Technology
Journal title :
Tsinghua Science and Technology
