A KGE Based Knowledge Enhancing Method for Aspect-Level Sentiment Classification

Haibo Yu, Guojun Lu, Qianhua Cai () and Yun Xue
Additional contact information
Haibo Yu: School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
Guojun Lu: School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
Qianhua Cai: 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 20, 1-12

Abstract: ALSC (Aspect-level Sentiment Classification) is a fine-grained task in the field of NLP (Natural Language Processing) which aims to identify the sentiment toward a given aspect. In addition to exploiting the sentence semantics and syntax, current ALSC methods focus on introducing external knowledge as a supplementary to the sentence information. However, the integration of the three categories of information is still challenging. In this paper, a novel method is devised to effectively combine sufficient semantic and syntactic information as well as use of external knowledge. The proposed model contains a sentence encoder, a semantic learning module, a syntax learning module, a knowledge enhancement module, an information fusion module and a sentiment classifier. The semantic information and syntactic information are respectively extracted via a self-attention network and a graphical convolutional network. Specifically, the KGE (Knowledge Graph Embedding) is employed to enhance the feature representation of the aspect. Then, the attention-based gate mechanism is taken to fuse three types of information. We evaluated the proposed model on three benchmark datasets and the experimental results establish strong evidence of high accuracy.

Keywords: aspect-level sentiment classification; external knowledge; KGE; GCN (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 (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/20/3908/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/20/3908/ (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:20:p:3908-:d:949371

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 ().