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An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification

文献类型：期刊论文

作者Lin, Zheng1; Jin, Xiaolong2; Xu, Xueke2; Wang, Yuanzhuo2; Cheng, Xueqi2; Wang, Weiping1; Meng, Dan1
刊名IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
出版日期2016-03-01
卷号24期号:3页码:13
关键词Cross-language sentiment classification topic model
ISSN号2329-9290
DOI10.1109/TASLP.2015.2512041
英文摘要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.
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
语种英语
WOS记录号WOS:000372025000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/8658]  
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.

入库方式： OAI收割

来源：计算技术研究所

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