Multi-channel Graph Convolution for Aspect-level Sentiment Classification of Online Student Reviews

Abstract—It is important for schools and online learning platforms to mine student attitudes and opinions from student reviews so teachers know what needs to be improved. Previous studies have only focused on the semantic features of sentences, ignoring syntactic dependency relations in sentences. To address this problem, we build a graph convolutional network (GCN) on the syntactic dependency tree of sentences to exploit syntactic information for the first time in the field of online student reviews. Furthermore, in order to exploit positional relations and alleviate over-smoothing, we add relative aspect distance relations and stochastic edge elimination. On this basis, a novel multi-channel graph convolution model (MCGCN) is proposed, which constructs three GCN channels for syntactic dependency relations, relative aspect distance relations and stochastic edge elimination to extract relational features. Finally we use average pooling to fuse three relational features. Experiments on the CR23k dataset demonstrate that the overall performance of the model outperforms the state-of-the-art methods.