Roaen: reversed dependency graph and orthogonal-gating strategy attention-enhanced network for aspect-level sentiment classification

Aspect-level sentiment classification aims to determine the sentiment polarity of different aspects within a sentence. Many existing approaches employ Graph Convolutional Networks in conjunction with dependency trees to model syntactic structures in sentences but often ignore its limitations in modeling long-distance words. This paper proposes a reversed dependency graph and orthogonal-gating strategy attention-enhanced network, which introduces a novel reversed dependency graph convolutional network combined with orthogonal projection (RGCN-O) to model long-distance words and syntactic structures simultaneously. Specifically, RGCN-O discards a few words with dependencies, generates vector representations for long-distance words, and models syntactic structures from its orthogonal space. The output of RGCN-O is then used to design a gating strategy that dynamically filters attention noise in a weighted manner. Additionally, we offer a novel total inter-intra loss function to enhance the compactness and distinctiveness of the intra-class and inter-class sample features. Extensive experiments on five benchmark datasets demonstrate the effectiveness of RGCN-O and the new loss function, with ROAEN achieving state-of-the-art performance.