Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
779-785

Dual-channel graph convolutional network with word-order knowledge for aspect-based sentiment analysis

Huang Jun
Liu Yang
Wang Qingfeng
Chen Liwei
Qiu Jialin
Li Maofeng
School of Computer Science & Technology, Southwest University of Science & Technology, Mianyang Sichuan 621010, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0310

Abstract

At present, most of the aspect-based sentiment analysis methods based on graph convolutional network use the syntactic knowledge, semantic knowledge and sentiment knowledge of text to construct text dependency, but few studies have made use of text word-order knowledge to build text dependency. As a result, graph convolutional networks can't effectively use text word order knowledge to guide aspects to learn contextual sentiment information, thus limiting its performance. Aiming at the above problems, this paper proposed an aspect-based sentiment analysis model of dual-channel graph convolutional network with word-order knowledge(WKDGCN), which consisted of WoGCN and SAGCN. Specifically, WoGCN constructed a graph convolutional network based on the word-order knowledge of text, and learned contextual sentiment information by relying on the characteristics of the guiding aspect of the text word order. SAGCN used the sentiment knowledge in SenticNet combined with the attention mechanism to enhance syntactic dependence, and used the enhanced syntactic dependence to construct a graph convolutional network, so as to guide the aspect features to learn contextual emotional information. Finally, it fused the features of two graph convolutional networks for sentiment classification. In addition, it designed a weight allocation strategy to keep the context weights consistent while amplifying the weights of the terms to avoid erroneous calculation of the semantic correlation between the features of the terms and the important features. Experimental results on several public data sets show that the proposed method is superior to the comparison models.

Foundation Support

四川省自然科学基金资助项目(2022NSFSC0940,2022NSFSC0894)
西南科技大学博士基金资助项目(20zx7137)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0310
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Algorithm Research & Explore
Pages: 779-785
Serial Number: 1001-3695(2024)03-019-0779-07

Publish History

[2023-09-27] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

黄俊, 刘洋, 王庆凤, 等. 基于语序知识的双通道图卷积网络方面级情感分析 [J]. 计算机应用研究, 2024, 41 (3): 779-785. (Huang Jun, Liu Yang, Wang Qingfeng, et al. Dual-channel graph convolutional network with word-order knowledge for aspect-based sentiment analysis [J]. Application Research of Computers, 2024, 41 (3): 779-785. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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