A Cross-Language Attribute-Level Sentiment Analysis Approach Using TinyBERT and GCN

Pan Zhou, Xingbin Qi and Li Zhao
Additional contact information
Pan Zhou: Guangzhou Huashang College, China
Xingbin Qi: Shanxi University, China
Li Zhao: Shanxi University, China

International Journal of Knowledge Management (IJKM), 2024, vol. 20, issue 1, 1-23

Abstract: A cross-language attribute-level sentiment analysis model (TinyBERT-GCN) based on TinyBERT and GCN is proposed to address the problems of existing cross-language attribute-level sentiment analysis methods, such as insufficient text feature extraction and easy to ignore cross-language semantic correlations at the word level. The model extracts contextual semantics through TinyBERT, fuses multilingual features using the Multi-Granular Interaction Module, and enhances the understanding of text syntax using GCN. The experimental results show that the proposed TinyBERT-GCN model can achieve ACC and F1 of 0.871 and 0.812 on SemEVAL-2016 dataset; and 0.848 and 0.821 on Taobao dataset, and 0.863 and 0.802 on SemEVAL-2014 dataset respectively. Compared with the other models, the proposed model not only improves the performance, but also reduces the computational cost, has better scalability, and is suitable for large-scale multilingual data processing. This model has important practical applications in market analysis, public opinion monitoring and decision making.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKM.360783 (application/pdf)

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:igg:jkm000:v:20:y:2024:i:1:p:1-23

Access Statistics for this article

International Journal of Knowledge Management (IJKM) is currently edited by Hakikur Rahman

More articles in International Journal of Knowledge Management (IJKM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().