Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification

doi:10.1109/TNNLS.2020.2979225

	Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification
	Wang, Deqing 1; Jing, Baoyu 2,4; Lu, Chenwei 1; Wu, Junjie 3; Liu, Guannan 3,4; Du, Chenguang 1; Zhuang, Fuzhen 5,6
	2021-02-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	32 期号:2 页码:736-747
摘要	Cross-lingual sentiment classification (CLSC) aims to leverage rich-labeled resources in the source language to improve prediction models of a resource-scarce domain in the target language. Existing feature representation learning-based approaches try to minimize the difference of latent features between different domains by exact alignment, which is achieved by either one-to-one topic alignment or matrix projection. Exact alignment, however, restricts the representation flexibility and further degrades the model performances on CLSC tasks if the distribution difference between two language domains is large. On the other hand, most previous studies proposed document-level models or ignored sentiment polarities of topics that might lead to insufficient learning of latent features. To solve the abovementioned problems, we propose a coarse alignment mechanism to enhance the model's representation by a group-to-group topic alignment into an aspect-level fine-grained model. First, we propose an unsupervised aspect, opinion, and sentiment unification model (AOS), which trimodels aspects, opinions, and sentiments of reviews from different domains and helps capture more accurate latent feature representation by a coarse alignment mechanism. To further boost AOS, we propose ps-AOS, a partial supervised AOS model, in which labeled source language data help minimize the difference of feature representations between two language domains with the help of logistics regression. Finally, an expectation-maximization framework with Gibbs sampling is then proposed to optimize our model. Extensive experiments on various multilingual product review data sets show that ps-AOS significantly outperforms various kinds of state-of-the-art baselines.
关键词	Data models Task analysis Semantics Learning systems Bridges Logistics Vocabulary Coarse alignment cross-lingual sentiment classification (CLSC) topic model
DOI	10.1109/TNNLS.2020.2979225
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1402800] ; National Natural Science Foundation of China[71501003] ; National Natural Science Foundation of China[71531001] ; National Natural Science Foundation of China[71725002] ; National Natural Science Foundation of China[U1636210] ; National Natural Science Foundation of China[U1836206] ; National Key R&D Program of China[2019YFB2101804] ; Project of Youth Innovation Promotion Association CAS[2017146]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000616310400022
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：23[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/2XEOYT63/16185
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhuang, Fuzhen
作者单位	1.Beihang Univ, Sch Comp Sci, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China 2.Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA 3.Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100864, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wang, Deqing,Jing, Baoyu,Lu, Chenwei,et al. Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(2):736-747.
APA	Wang, Deqing.,Jing, Baoyu.,Lu, Chenwei.,Wu, Junjie.,Liu, Guannan.,...&Zhuang, Fuzhen.(2021).Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(2),736-747.
MLA	Wang, Deqing,et al."Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.2(2021):736-747.