Knowledge-Enhanced Dual-Channel GCN for Aspect-Based Sentiment Analysis

Zhengxuan Zhang, Zhihao Ma, Shaohua Cai (), Jiehai Chen and Yun Xue
Additional contact information
Zhengxuan Zhang: School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
Zhihao Ma: Wechat Open Platform Department, Tencent, Guangzhou 510220, China
Shaohua Cai: Center for Faculty Development, South China Normal University, Guangzhou 510631, China
Jiehai Chen: School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
Yun Xue: School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China

Mathematics, 2022, vol. 10, issue 22, 1-15

Abstract: As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks.

Keywords: aspect-based sentiment analysis; graph convolutional networks; commonsense knowledge graph (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/22/4273/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/22/4273/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:22:p:4273-:d:973462

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().