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<p>Aspect-based sentiment analysis (ABSA) aims to identify aspect terms and determine their sentiment polarity. While dependency trees combined with contextual semantics provide structural cues, existing ABSA approaches often rely on dot-product similarity and fixed graphs, which limit their ability to capture nonlinear associations and adapt to noisy contexts. To address these limitations, we propose the Syntactic-optimal Transport Graph Network (SOT-Graph), a model that jointly integrates structural and distributional signals. Specifically, a syntactic graph-aware attention module models global dependencies with syntax-guided masking, while a semantic optimal transport attention module formulates aspect-opinion association as a distribution matching problem solved via the Sinkhorn algorithm. An adaptive attention fusion mechanism balances heterogeneous features, and contrastive regularization enhances robustness. Extensive experiments on four benchmark datasets (Rest14, Laptop14, Twitter and METS-CoV-TSA) demonstrate that SOT-Graph achieves state-of-the-art performance. Notably, it outperforms existing baselines by a margin of 1.30% Macro-F1 on Laptop14 and 1.01% on Twitter. Ablation studies and visualization analyses further confirm SOT-Graph’s superiority in capturing fine-grained sentiment associations while effectively filtering out contextual noise.</p>

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Feature-level enhanced syntactic-semantic graph networks via optimal transport for aspect-based sentiment analysis

  • 期刊论文

  • 发表日期：

    2026年7月4日

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  • Xinfeng Liao,
  • Xuanqi Chen,
  • Lianxi Wang,
  • Ziying Rong,
  • Jiahuan Yang,
  • Jie Li,
  • Zhuowei Chen

摘要

Aspect-based sentiment analysis (ABSA) aims to identify aspect terms and determine their sentiment polarity. While dependency trees combined with contextual semantics provide structural cues, existing ABSA approaches often rely on dot-product similarity and fixed graphs, which limit their ability to capture nonlinear associations and adapt to noisy contexts. To address these limitations, we propose the Syntactic-optimal Transport Graph Network (SOT-Graph), a model that jointly integrates structural and distributional signals. Specifically, a syntactic graph-aware attention module models global dependencies with syntax-guided masking, while a semantic optimal transport attention module formulates aspect-opinion association as a distribution matching problem solved via the Sinkhorn algorithm. An adaptive attention fusion mechanism balances heterogeneous features, and contrastive regularization enhances robustness. Extensive experiments on four benchmark datasets (Rest14, Laptop14, Twitter and METS-CoV-TSA) demonstrate that SOT-Graph achieves state-of-the-art performance. Notably, it outperforms existing baselines by a margin of 1.30% Macro-F1 on Laptop14 and 1.01% on Twitter. Ablation studies and visualization analyses further confirm SOT-Graph’s superiority in capturing fine-grained sentiment associations while effectively filtering out contextual noise.