Abstract: Aspect-based sentiment analysis(ABSA) is a fine-grained task in sentiment analysis that aims to detect the emotional polarity of aspects in given sentence.Due to the rise of deep learning and graph convolutional networks(GCNs),GCN constructed over dependency tree has been widely applied to ABSA and achieved satisfactory results.However,most studies only acquire the last layer node features of graph convolutional network(GCN) as input to the classifier,while ignoring other layer node features and GCNs have over-smoothing problem.In recent years,some researchers ensembled the multilayer node features of GCN,improving the performance of sentiment classification models.A model combines adaptively spatial feature fusion and highway networks,namely highway graph convolutional network based on multi-granularity feature fusion(MGFF-HGCN) is proposed for ABSA in this paper.First,this model constructs GCN by syntactic dependency structure and bidirectional context information,and highway networks is introduced for alleviating the deep GCN over-smoothing problem,deepening the depth of GCN.Then,a adaptive fusion mechanism is effectively employed to fuse the more comprehensive and multi-granularity node feature information obtained from various highway GCN(HGCN) layers.Finally,experimental results on public datasets show that the proposed method is comparable to the benchmark models and be able to capture more granular syntactic information and long-range dependencies relationship accurately.

Key words: Multi-granularity, Feature fusion, Graph convolutional networks, Highway networks, Aspect-based sentiment analysis