A Triple-GCN: Enhanced Multi-Feature Graph Convolutional Network for Aspect-Based Sentiment Analysis

Authors

  • Huanling Tang School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
  • Xueyuan Sun School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, China
  • Quansheng Dou School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
  • Mingyu Lu School of Information Science and Technology, Dalian Maritime University, Dalian, China

Keywords:

Aspect-based sentiment analysis, graph convolutional network, attention mechanism, dependency tree, common information, shared weight matrix

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 enhancd 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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Published

2025-06-30

How to Cite

Tang, H., Sun, X., Dou, Q., & Lu, M. (2025). A Triple-GCN: Enhanced Multi-Feature Graph Convolutional Network for Aspect-Based Sentiment Analysis. Computing and Informatics, 44(3). Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/7146