Abstract

Aspect-Based Sentiment Analysis (ABSA) aims to predict the sentiment polarity of the given aspect word within the sentence. Recent studies frequently treat syntactic and semantic features as independent representations, thereby overlooking their intrinsic correlation. Concurrently, most of the existing methods largely neglect the significance of dependency types, which eventually impacts the accuracy of sentiment analysis. Research based on cognitive theory indicates a mutual influence between syntax and semantics. Based on this, we propose an ABSA model based on enhanced multi-feature graph convolutional network (Triple-GCN). Firstly, a shared enhanced graph convolutional module is proposed to integrate syntactic and semantic information. Following this, a thorough fusion of this syntactic and semantic information is carried out. Besides, relation and adjacency matrices are utilized for the innovative reconstruction of hidden state vectors. Syntactic graph convolution module dynamically fuses hidden state vectors and dependency features. Additionally, a position weight encoding function is designed to comprehend sentiment dependencies by drawing attention to aspect-near words. On the semantic side, dynamic semantic graphs are constructed, enabling the capture of semantic features. The model has been evaluated on three public datasets: Twitter, Laptop14, and Restaurant14. Compared to existing baseline models, the effectiveness of this model has noticeably improved.

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Cite this article

@article{cai-7146,
  author  = {Huanling Tang and Xueyuan Sun and Quansheng Dou and Mingyu Lu},
  title   = {Triple-GCN: Enhanced Multi-Feature Graph Convolutional Network for Aspect-Based Sentiment Analysis},
  journal = {Computing and Informatics},
  volume  = {44},
  number  = {3},
  pages   = {493--522},
  year    = {2025},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2025_3_493}
}
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