Aspect-Based Sentiment Analysis via Virtual Node Augmented Graph Convolutional Networks

Aspect-based sentiment analysis (ABSA) refers to a finegrained sentiment analysis task aimed at detecting sentiment polarity towards a given aspect. Recently, graph convolutional networks (GCN) integrated with dependency trees have achieved related appealing results in ABSA. Nevertheless, most existing models fail to preserve the information of the whole graph although global information can often significantly improve their performance. To address this problem, a novel virtual node augmented graph convolutional network (ViGCN) is proposed to further enhance the performance of GCNs in the ABSA task by adding a virtual node to the graph. The virtual node can connect to all the nodes in the graph to aggregate global information from the entire graph and then propagate it to each node. In particular, we construct edges between the virtual node and other nodes based on affective commonsense knowledge from SenticNet and the semantic-relative distances between contextual words and the aspect, effectively enhancing the collected global information towards the given aspect. Extensive experiments on three benchmark datasets illustrate that the ViGCN model can beat state-of-the-art models, proving its effectiveness.