Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network

The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis (ABSA). However, the accuracy of the dependency parser cannot be determined, which may keep aspect words away from its related opinion words in a dependency tree. Moreover, few models incorporate external affective knowledge for ABSA. Based on this, we propose a novel architecture to tackle the above two limitations, while fills up the gap in applying heterogeneous graphs convolution network to ABSA. Specially, we employ affective knowledge as an sentiment node to augment the representation of words. Then, linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency tree. Finally, we design a multi-level semantic heterogeneous graph convolution network (Semantic-HGCN) to encode the heterogeneous graph for sentiment prediction. Extensive experiments are conducted on the datasets SemEval 2014 Task 4, SemEval 2015 task 12, SemEval 2016 task 5 and ACL 14 Twitter. The experimental results show that our method achieves the state-of-the-art performance.

Automated summary

Deep learning methods based on syntactic dependency trees have achieved great success in Aspect-based Sentiment Analysis (ABSA). However, the lack of accuracy in dependency parsers may cause aspect words to be separated from related opinion words. Additionally, few models incorporate external affective knowledge in ABSA. To address these limitations, we propose a novel architecture that fills the gap in using heterogeneous graph convolution networks for ABSA, by employing affective knowledge to enhance word representation and constructing a heterogeneous graph based on dependency trees. Our proposed method, the Semantic-HGCN, achieves state-of-the-art performance in sentiment prediction according to extensive experiments on multiple datasets.