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Relation construction for aspect-level sentiment classification
Zeng, Jiandian1; Liu, Tianyi2; Jia, Weijia2,3; Zhou, Jiantao1
2022-03-01
Source PublicationInformation Sciences
ISSN0020-0255
Volume586Pages:209-223
Abstract

Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of different aspects in one sentence. Most existing approaches handle the classification by acquiring the importance of context words towards each given aspect individually, and ignore the benefits brought by aspect relations. Since the sentiment of one aspect can be deduced through their relationship according to other aspects, in this paper, we propose a novel relation construction multi-task learning network (RMN), which is the first attempt to extract aspect relations as an auxiliary classification task. RMN generates aspect representations through graph convolution networks with a semantic dependency graph and utilizes the bi-attention mechanism to capture the relevance between the aspect and the context. Unlike conventional multi-task learning methods that need extra datasets, we construct an auxiliary relation-level classification task that extracts aspect relations from the original dataset with shared parameters. Extensive experiments on five public datasets from SemEval 14, 15, 16 and MAMS show that our RMN improves about 0.09% to 0.8% on accuracy and about 0.04% to 1.19% on F1 score, compared to several comparative baselines.

KeywordAspect Relations Aspect-level Graph Convolutional Network Sentiment Analysis
DOI10.1016/j.ins.2021.11.081
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000794186300012
Scopus ID2-s2.0-85120964385
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorZhou, Jiantao
Affiliation1.State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau, China
2.School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
3.BNU-UIC Joint AI Research Institute, Beijing Normal University, Guangdong, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zeng, Jiandian,Liu, Tianyi,Jia, Weijia,et al. Relation construction for aspect-level sentiment classification[J]. Information Sciences,2022,586:209-223.
APA Zeng, Jiandian,Liu, Tianyi,Jia, Weijia,&Zhou, Jiantao.(2022).Relation construction for aspect-level sentiment classification.Information Sciences,586,209-223.
MLA Zeng, Jiandian,et al."Relation construction for aspect-level sentiment classification".Information Sciences 586(2022):209-223.
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