An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification

doi:10.1109/TASLP.2015.2512041

	An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification
	Lin, Zheng 1; Jin, Xiaolong 2; Xu, Xueke 2; Wang, Yuanzhuo 2; Cheng, Xueqi 2; Wang, Weiping 1; Meng, Dan 1
	2016-03-01
发表期刊	IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
ISSN	2329-9290
卷号	24 期号:3 页码:13
摘要	Sentiment classification aims to determine the sentiment polarity expressed in a text. In online customer reviews, the sentiment polarities of words are usually dependent on the corresponding aspects. For instance, in mobile phone reviews, we may expect the long battery time but not enjoy the long response time of the operating system. Therefore, it is necessary and appealing to consider aspects when conducting sentiment classification. Probabilistic topic models that jointly detect aspects and sentiments have gained much success recently. However, most of the existing models are designed to work well in a language with rich resources. Directly applying those models on poor-quality corpora often leads to poor results. Consequently, a potential solution is to use the cross-lingual topic model to improve the sentiment classification for a target language by leveraging data and knowledge from a source language. However, the existing cross-lingual topic models are not suitable for sentiment classification because sentiment factors are not considered therein. To solve these problems, we propose for the first time a novel cross-lingual topic model framework which can be easily combined with the state-of-the-art aspect/sentiment models. Extensive experiments in different domains and multiple languages demonstrate that our model can significantly improve the accuracy of sentiment classification in the target language.
关键词	Cross-language sentiment classification topic model
DOI	10.1109/TASLP.2015.2512041
收录类别	SCI
语种	英语
资助项目	973 Program of China[2012CB316303] ; 973 Program of China[2014CB340401] ; National Natural Science Foundation of China[61502478] ; National Natural Science Foundation of China[61572469] ; National Natural Science Foundation of China[61572473] ; National Natural Science Foundation of China[61232010] ; National Natural Science Foundation of China[61173008] ; National High-Tech Research and Development Program of China[2013AA013204] ; National HeGaoJi Key Project[2013ZX01039-002-001-001]
WOS研究方向	Acoustics ; Engineering
WOS类目	Acoustics ; Engineering, Electrical & Electronic
WOS记录号	WOS:000372025000001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8659
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Lin, Zheng; Jin, Xiaolong; Xu, Xueke; Wang, Yuanzhuo; Cheng, Xueqi; Wang, Weiping; Meng, Dan
作者单位	1.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Lin, Zheng,Jin, Xiaolong,Xu, Xueke,et al. An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2016,24(3):13.
APA	Lin, Zheng.,Jin, Xiaolong.,Xu, Xueke.,Wang, Yuanzhuo.,Cheng, Xueqi.,...&Meng, Dan.(2016).An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification.IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,24(3),13.
MLA	Lin, Zheng,et al."An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification".IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 24.3(2016):13.