Research Area:  Machine Learning
Aspect-level sentiment classification is a fundamental subtask of fine-grained sentiment analysis. The syntactic information and commonsense knowledge are important and useful for aspect-level sentiment classification, while only a limited number of studies have explored to incorporate them via flexible graph convolutional neural networks (GCN) for this task. In this paper, we propose a new Syntax- and Knowledge-based Graph Convolutional Network (SK-GCN) model for aspect-level sentiment classification, which leverages the syntactic dependency tree and commonsense knowledge via GCN. In particular, to enhance the representation of the sentence toward the given aspect, we develop two strategies to model the syntactic dependency tree and commonsense knowledge graph, namely SK-GCN and SK-GCN respectively. SK-GCN models the dependency tree and knowledge graph via Syntax-based GCN (S-GCN) and Knowledge-based GCN (K-GCN) independently, and SK-GCN models them jointly. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Extensive experiments on five benchmark datasets demonstrate that our approach can effectively improve the performance of aspect-level sentiment classification compared with the state-of-the-art methods.
Author(s) Name:  Jie Zhou,Jimmy Xiangji Huang,Qinmin Vivian Hu,Liang He
Journal name:  Knowledge-Based Systems
Publisher name:  Elsevier
Volume Information:  Volume 205, 12 October 2020, 106292
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0950705120304688