Dual-channel relative position guided attention networks for aspect-based sentiment analysis

doi:10.1016/j.eswa.2024.124271

	Dual-channel relative position guided attention networks for aspect-based sentiment analysis
	Gao, Xuejian 1; Liu, Fang'ai 1; Zhuang, Xuqiang 2; Tian, Xiaohui 1; Zhang, Yujuan 1; Liu, Kenan 3
	2024-11-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	253 页码:14
摘要	Aspect -based sentiment analysis (ABSA) aims to match sentiment tendencies for different aspects of a sentence to understand the product experience of the user. It is a pressing challenge for existing ABSA methods to synthesize sentences' semantic relevance and syntactic dependency for more comprehensive sentiment representations. In this paper, we propose a Dual -Channel Relative Position Guided Attention Network (DualRPGA). Dual-RPGA deeply learns semantic and syntactic representations of sentiment to provide reliable knowledge for dynamic fusion and prediction of sentiment. First, we design a syntactic graph attention network (Syn-GAT) to learn the syntactic relative position between aspect and context, which guides the sentiment syntactic representation. Then, we build a semantic attention network (Sem -Attention). It computes semantic attention and similarity coefficients for aspects and contexts to enhance sentiment semantic expressions. Finally, we design a fusion network (Bi-Fusion) that realizes dynamic feature interactions of sentiment semantics and syntactics to perform sentiment prediction. We conduct extensive experiments on two groups of datasets to validate the performance of Dual-RPGA on the ABSA task. The results show that Dual-RPGA outperforms the optimal baseline by 0.58% similar to 1.49% of the Acc score, which verifies that Dual-RPGA performs better on the ABSA task.
关键词	Aspect-based sentiment analysis Graph neural network Semantic correlation Syntactic dependency
DOI	10.1016/j.eswa.2024.124271
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of Shandong Province[ZR202011020044] ; National Natural Science Foundation of China[61772321]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:001247600800004
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39919
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Fang'ai
作者单位	1.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China 2.Shandong Normal Univ, Off Informatizat, Jinan 250014, Shandong, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Gao, Xuejian,Liu, Fang'ai,Zhuang, Xuqiang,et al. Dual-channel relative position guided attention networks for aspect-based sentiment analysis[J]. EXPERT SYSTEMS WITH APPLICATIONS,2024,253:14.
APA	Gao, Xuejian,Liu, Fang'ai,Zhuang, Xuqiang,Tian, Xiaohui,Zhang, Yujuan,&Liu, Kenan.(2024).Dual-channel relative position guided attention networks for aspect-based sentiment analysis.EXPERT SYSTEMS WITH APPLICATIONS,253,14.
MLA	Gao, Xuejian,et al."Dual-channel relative position guided attention networks for aspect-based sentiment analysis".EXPERT SYSTEMS WITH APPLICATIONS 253(2024):14.