research-article

Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network

Authors:

Huifang MaAuthors Info & Claims

Frontiers of Computer Science, Volume 17, Issue 6

https://doi.org/10.1007/s11704-022-2256-5

Published: 21 January 2023 Publication History

Abstract

The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis (ABSA). However, the accuracy of the dependency parser cannot be determined, which may keep aspect words away from its related opinion words in a dependency tree. Moreover, few models incorporate external affective knowledge for ABSA. Based on this, we propose a novel architecture to tackle the above two limitations, while fills up the gap in applying heterogeneous graphs convolution network to ABSA. Specially, we employ affective knowledge as an sentiment node to augment the representation of words. Then, linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency tree. Finally, we design a multi-level semantic heterogeneous graph convolution network (Semantic-HGCN) to encode the heterogeneous graph for sentiment prediction. Extensive experiments are conducted on the datasets SemEval 2014 Task 4, SemEval 2015 task 12, SemEval 2016 task 5 and ACL 14 Twitter. The experimental results show that our method achieves the state-of-the-art performance.

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cover image Frontiers of Computer Science: Selected Publications from Chinese Universities

Frontiers of Computer Science: Selected Publications from Chinese Universities Volume 17, Issue 6

Dec 2023

184 pages

ISSN:2095-2228

EISSN:2095-2236

Issue’s Table of Contents

© Higher Education Press 2023.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 21 January 2023

Accepted: 12 October 2022

Received: 01 May 2022

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Cited By

Liu FBu CZhang HWu LYu KHu X(2024)FDKT: Towards an Interpretable Deep Knowledge Tracing via Fuzzy ReasoningACM Transactions on Information Systems10.1145/365616742:5(1-26)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3656167
Ou LLi ZGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Modeling Multi-Task Joint Training of Aggregate Networks for Multi-Modal Sarcasm DetectionProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658015(833-841)Online publication date: 30-May-2024
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Zhuang XZhou FLi ZSerra ESpezzano F(2024)MV-BART: Multi-view BART for Multi-modal Sarcasm DetectionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679570(3602-3611)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679570
Wu CCao LChen JWang YSu J(2024)Modeling different effects of user and product attributes on review sentiment classificationApplied Intelligence10.1007/s10489-023-05236-654:1(835-850)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05236-6
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https://dl.acm.org/doi/10.1145/3650215.3650286

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