App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Emotional and semantic enhances syntactic features in weighted adaptive graph convolutional networks for aspect-based sentiment analysis

Shi Yuemei1
Liu Yang2
Zheng Lijuan3
Pu Jiuliang2
1. School of Artificial Intelligence and Big Data, Chengdu College of Arts and Sciences, Chengdu Sichuan 610401, China
2. School of Computer Engineering, Sichuan Institute of Industrial Technology, Deyang Sichuan 618500, China
3. School of Artificial Intelligence and Electronic Engineering, Sichuan Technology and Business University, Chengdu Sichuan 611745, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0060

Abstract

Current aspect-level sentiment analysis focuses on using graph convolutional networks to integrate multiple features, such as syntax and semantics, to enhance sentiment classification performance. However, pure syntactic features lack correlation analysis among words and fail to capture the differential contribution of individual words to the overall semantic expression of a text. To address these issues, this approach enhances syntactic features with sentiment and semantic information by respectively constructing a Semantic Graph Convolutional Networks (SemGCN) module and an Emotional Graph Convolutional Networks (EmoGCN) module. It then fuses these two modules to build a graph convolutional network—Emotional and Semantic Enhances Syntactic features in Weighted Adaptive Graph Convolutional Networks (ESEWAGCN) —which improves the model’s ability to comprehend the text. Furthermore, based on the TF-IDF algorithm, this method designs a Weighted Adaptive Mechanism (WAM) that adaptively assigns weights according to word importance, thereby reflecting the varying contribution of different words to the overall semantic expression of the text. Extensive experimental results on public datasets demonstrate that our model achieves superior performance compared to baseline models.

Foundation Support

全国重点实验室开放课题基金资助项目(KFJJ202403)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0060
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-30] Accepted Paper

Cite This Article

时月梅, 刘洋, 郑丽娟, 等. 情感语义增强句法特征的权重自适应图卷积网络方面级情感分析 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0060. (Shi Yuemei, Liu Yang, Zheng Lijuan, et al. Emotional and semantic enhances syntactic features in weighted adaptive graph convolutional networks for aspect-based sentiment analysis [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0060. )

About the Journal

  • 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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