Dual-Level Attention Based on Heterogeneous Graph Convolution Network for Aspect-Based Sentiment Classification

We introduce a flexible HIN (Heterogeneous Information Network) framework to model user-generated comments. It can integrate various types of additional information and capture the relationship between them to reduce the semantic sparsity of a small amount of labeled data. It can also take advantage of the hidden network structure information by spreading the information together with the graph. Then we propose to use a dual-level attention-based heterogeneous convolutional graph network to understand the importance of different adjacent nodes and of different types of nodes to the current node. By doing this, we can mitigate the shortcomings that most existing algorithms ignore, i.e. the network structure information between the words in the sentence and the sentence itself. The experimental results on the SemEval dataset prove the validity and reliability of our model.