Extracting Aspect-Based Economic Sentiments from Thai Social Media Text
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85201434896
Journal Title
Proceedings - 21st International Joint Conference on Computer Science and Software Engineering, JCSSE 2024
Start Page
373
End Page
376
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SCOPUS
Bibliographic Citation
Proceedings - 21st International Joint Conference on Computer Science and Software Engineering, JCSSE 2024 (2024) , 373-376
Suggested Citation
Faengrit W., Tuarob S., Noraset T. Extracting Aspect-Based Economic Sentiments from Thai Social Media Text. Proceedings - 21st International Joint Conference on Computer Science and Software Engineering, JCSSE 2024 (2024) , 373-376. 376. doi:10.1109/JCSSE61278.2024.10613669 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/100594
Title
Extracting Aspect-Based Economic Sentiments from Thai Social Media Text
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Author's Affiliation
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Abstract
The pervasive impact of economics can be observed in online discussions on social media. Studies have shown that the ability to analyze social media text's overall sentiment or sentiment of a specific aspect is crucial for monitoring and predicting economic phenomena such as inflation and GDP. However, sentiment and aspect-based sentiment analyses in social media text composed in Thai are understudied mainly due to the lack of large-scale labeled datasets. This research proposes using the pairing encoding sentence for pre-trained language models (PLM) that utilize the Aspect Companion technique and transformer models to predict social media text's overall sentiment and aspect-category sentiments related to economics. Specifically, the proposed approach can train a language model that learns all aspect categories altogether. The results show that the Aspect Companion technique combined with WangchanBERTa outperformed other models in identifying polarity in overall economic sentiment and aspect-category sentiment analyses.