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

Aspect-based sentiment analysis with collaborative enhancement of pruned syntactic dependency and amr

Liu Xichang
Zhang Hui
Hu Po
School of Computer Science, Central China Normal University, Wuhan 430079, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0084

Abstract

To address the problems that large language model-based aspect-level sentiment analysis methods struggle to focus on structural information related to target aspects and make insufficient use of deep semantic relations in complex sentences, this paper proposed PSA-CoT, a three-stage chain-of-thought reasoning method enhanced by pruned syntactic dependency and AMR. The method first constructed a target aspect-oriented syntactic pruning strategy. It extracted key paths related to aspect terms and sentiment expressions from the original syntactic dependency tree, reducing noise caused by irrelevant nodes and redundant structures. Then, it designed a progressive reasoning framework of “aspect-related information extraction—opinion semantic inference—polarity label generation”. Pruned syntactic information and AMR semantic information were introduced into different reasoning stages to achieve collaborative modeling of local structure localization and deep semantic induction. Experimental results on the Rest14 and Lap14 datasets show that the proposed method improves accuracy and F1 scores on models such as GPT-3.5 and Qwen2-7B, and outperforms comparison methods in most settings. These results verify the effectiveness of collaborative enhancement using pruned syntactic dependency and AMR in improving the target-aspect sentiment polarity discrimination ability and reasoning interpretability of large language models.

Foundation Support

中央高校基本科研业务费项目(项目编号:CCNU24ai011)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0084
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-17] Accepted Paper

Cite This Article

刘希畅, 张慧, 胡珀. 基于剪枝句法依存与AMR协同增强的方面级情感分析 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0084. (Liu Xichang, Zhang Hui, Hu Po. Aspect-based sentiment analysis with collaborative enhancement of pruned syntactic dependency and amr [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0084. )

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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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