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TrustSyn: Augmenting LLMs With Constituency- Structured Dependency Knowledge for Aspect-Based Sentiment Analysis
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DOI：10.1109/TKDE.2026.3700652.png)
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Abstract
En 中文
Aspect-based sentiment analysis (ABSA) constitutes a critical subtask within affective computing, whose central challenge involves the accurate and efficient identification of sentiment polarity associated with specific aspect terms in review sentences. Although syntactic knowledge has demonstrated significant benefits in traditional ABSA models, existing approaches based on large language models (LLMs) have largely overlooked such structural information and often fail to comprehensively model both implicit and explicit sentiment expressions. To bridge this gap, we propose TrustSyn, a novel framework designed to enhance LLMs with trustworthy, constituency-structured dependency knowledge for ABSA. Specifically, the input sentences are first parsed using both dependency and constituency parsers. The resulting syntactic information is then restructured into a unified and reliable representation through a trustworthy syntax integration process. This structured knowledge is formalized and injected into LLMs to augment their comprehension of aspect-sentiment associations. To the best of our knowledge, this is the first work to integrate constituency-informed dependency structures into LLMs for ABSA. Finally, experimental results demonstrate that TrustSyn consistently outperforms state-of-the-art models across five benchmark datasets. Further ablation studies and analyses confirm its robustness and strong generalization capability.
Keywords:
Aspect-based sentiment analysis
constituency-structured dependency knowledge
syntax-augmented LLMs
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