GT-AGCN: Integrating Global Semantics and Local Syntax for Aspect-Based Sentiment Analysis

Mining sentiment from user-generated social media content is crucial for fields such as business decision-making and public opinion analysis. Aspect-based sentiment analysis (ABSA) aims to fine-grainedly identify the sentiment of different entities or attributes in texts. However, existing methods face two challenges: 1) attention mechanisms are prone to interference from aspect-irrelevant words, leading to attention bias; and 2) irrelevant syntactic dependencies are frequently introduced by graph convolutional networks constructed upon dense dependency trees, causing semantic interference. To address these challenges, this article proposes a novel method that combines graph transformer with aspect-oriented graph convolutional network (GT-AGCN), reducing attention bias and semantic interference while modeling global semantics and local syntax. First, a context encoder is constructed to capture contextualized semantic representations of the input text sequences. Second, to efficiently model global semantic information, a syntactically constrained Transformer module is developed to alleviate the inherent attention bias. Meanwhile, to accurately capture local syntax information, an aspect-oriented graph convolutional network is designed to suppress interference from aspect-irrelevant terms. Finally, a classifier is utilized to predict the sentiment polarities of target aspects by combining global semantic features with local syntactic features. Empirical evaluations across five widely-adopted ABSA benchmarks demonstrate that GT-AGCN outperforms state-of-the-art baselines, validating the effectiveness of model.