SEPGCN: tri-graph convolutional network for aspect-based sentiment analysis

Purpose This paper aims to address the limitations of existing aspect-based sentiment analysis (ABSA) approaches. ABSA is a fine-grained sentiment analysis task that is designed to predict the sentiment polarity of a given aspect within a sentence. Most existing approaches focus on modeling a single semantic or syntactic structure, failing to achieve comprehensive integration of multi-source information. In particular, positional information is often inadequately modeled, thereby limiting the ability to capture spatial relationships between opinion words and target aspects. Design/methodology/approach A novel tri-graph convolutional network, referred to as the Semantic, Emotional and Positional Graph Convolutional Network (SEPGCN), is proposed to address these limitations. The framework consists of a semantic graph convolutional network, an emotion-enhanced graph convolutional network and a position-enhanced graph convolutional network, which extract aspect-related features from semantic, emotional and positional perspectives. A dependency-centrality-based position fusion module is introduced to strengthen spatial awareness. Additionally, a Tri-Affine interaction module is used to facilitate deep interaction and fusion among heterogeneous features. Findings Experimental results obtained on three benchmark data sets indicate that SEPGCN outperforms representative baseline models in terms of accuracy and F1 score. In particular, it achieves up to 0.48% improvement in Accuracy and 1.22% improvement in F1 score over the strongest baselines across the datasets, while also yielding consistent gains in performance. Originality/value This study presents a tri-graph framework that jointly models semantic, emotional and positional information within a unified architecture. The findings highlight the role of positional modeling in capturing spatial associations between aspects and opinion expressions.