Enhancing Aspect Sentiment Classification with Dual-Channel Graph Convolutional Network

Aspect sentiment classification (ASC) constitutes a crucial research area within sentiment analysis tasks, aiming to predict sentiment polarity towards different aspects in given contexts. Identifying the relations between aspects and sentiments can be a challenging task, as aspects and sentiments are not always predefined. Most existing studies have demonstrated the effectiveness of using dependency parsing tree and graph convolutional network (GCN), achieving good experimental results. However, existing methods have mainly focused on either semantic or syntactic information individually, and may introduce errors when the input sentence lacks clear syntactic information. To address these issues, we propose a novel approach based on Dual-Channel Graph Convolutional Network (DC-GCN), which integrates feature fusion within a dual-channel architecture. Our model can effectively capture the semantic information and enhance the feature representation of syntactic structures by introducing the multi-head self-attention graph convolution, guided by the TopK strategy, and the directional densely connected graph convolutional network. We further employ a bi-affine strategy and multi-layer perceptron to integrate semantic and syntactic information. Experimental results on publicly available datasets demonstrate the superior performance of our model over state-of-the-art methods. Specifically, our model improves upon baseline models on the Twitter, Lap14, Rest14, Rest15, and Rest16 datasets, with increases in accuracy/macro-F1 scores of 0.06/0.58, 0.58/0.47, 0.25/1.19, 0.23/1.05, and 0.36/1.32, respectively.