Aggregated graph convolutional networks for aspect-based sentiment classification

Aspect-based sentiment analysis is a classic fine-grained approach that aims to distinguish sentiment polarities towards a particular aspect target. The majority of research on this topic has been devoted to constructing syntax-based graph convolutional networks (GCNs) for context feature vectors. These approaches perform poorly in terms of node representation and capturing long-distance dependency. In this paper, we focus on the ability of graph convolution and propose an aggregated graph convolutional network (AGCN) to enhance the representation ability of target nodes. To exploit the node feature information, we introduce two aggregator functions to iteratively update the representation of each node from its local neighborhood. To extract more associated node information, we also apply the subdependency of nodes to aggregate the node features, and then employ the attention mechanism to capture the sentiment dependencies between different node feature information. The proposed AGCN is evaluated on large Chinese and English datasets to prove the effect of our model in aspect-based sentiment analysis. The experimental results show that our model is valid compared with other GCN-based methods.