Exploring rich structure information for aspect-based sentiment classification

doi:10.1007/s10844-022-00729-1

	Exploring rich structure information for aspect-based sentiment classification
	Zhu, Ling 1; Zhu, Xiaofei 1; Guo, Jiafeng 2; Dietze, Stefan 3,4
	2022-07-30
发表期刊	JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
ISSN	0925-9902
页码	21
摘要	Graph Convolutional Network (GCN) for aspect-based sentiment classification has attracted a lot of attention recently due to their promising performance in handling complex structure information. However, previous methods based on GCN focused mainly on examining the structure of syntactic dependency relationships, which were subject to the noise and sparsity problem. Furthermore, these methods tend to focus on one kind of structural information (namely syntactic dependency) while ignoring many other kinds of rich structures between words. To tackle these problems, we propose a novel GCN based model, named Structure-Enhanced Dual-Channel Graph Convolutional Network (SEDC-GCN). Specifically, we first exploit the rich structure information by constructing a text sequence graph and an enhanced dependency graph, then design a dual-channel graph encoder to model the structure information from the two graphs. After that, we propose two kinds of aspect-specific attention, i.e., aspect-specific semantic attention and aspect-specific structure attention, to learn sentence representation from two different perspectives, i.e., the semantic perspective based on the text encoder, and the structure perspective based on the dual-channel graph encoder. Finally, we merge the sentence representations from the above two perspectives and obtain the final sentence representation. We experimentally validate our proposed model SEDC-GCN by comparing with seven strong baseline methods. In terms of the metric accuracy, SEDC-GCN achieves performance gains of 74.42%, 77.74%, 83.30%, 81.73% and 90.75% on TWITTER, LAPTOP, REST14, REST15, and REST16, respectively, which are 0.35%, 4.22%, 1.62%, 0.70% and 2.01% better than the best performing baseline BiGCN. Similar performance improvements are also observed in terms of the metric macro-averaged F1 score. The ablation study further demonstrates the effectiveness of each component of SEDC-GCN.
关键词	Aspect-based sentiment classification Graph convolutional networks Attention mechanism Sentiment analysis
DOI	10.1007/s10844-022-00729-1
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62141201] ; Federal Ministry of Education and Research[01IS21086]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000833443000001
出版者	SPRINGER
引用统计	被引频次：12[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/2XEOYT63/19491
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Xiaofei
作者单位	1.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Heinrich Heine Univ Dusseldorf, Inst Comp Sci, D-40225 Dusseldorf, Germany 4.Leibniz Inst Social Sci, Knowledge Technol Social Sci, D-50667 Cologne, Germany
推荐引用方式 GB/T 7714	Zhu, Ling,Zhu, Xiaofei,Guo, Jiafeng,et al. Exploring rich structure information for aspect-based sentiment classification[J]. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS,2022:21.
APA	Zhu, Ling,Zhu, Xiaofei,Guo, Jiafeng,&Dietze, Stefan.(2022).Exploring rich structure information for aspect-based sentiment classification.JOURNAL OF INTELLIGENT INFORMATION SYSTEMS,21.
MLA	Zhu, Ling,et al."Exploring rich structure information for aspect-based sentiment classification".JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2022):21.