Detecting Depression in Thai Blog Posts: a Dataset and a Baseline

Mika Hämäläinen, Pattama Patpong, Khalid Alnajjar, Niko Partanen, Jack Rueter


Abstract
We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53% accuracy with a Thai BERT model in detecting depression. This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia. Our corpus, code and trained models have been released openly on Zenodo.
Anthology ID:
2021.wnut-1.3
Volume:
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
Month:
November
Year:
2021
Address:
Online
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–25
Language:
URL:
https://aclanthology.org/2021.wnut-1.3
DOI:
10.18653/v1/2021.wnut-1.3
Bibkey:
Cite (ACL):
Mika Hämäläinen, Pattama Patpong, Khalid Alnajjar, Niko Partanen, and Jack Rueter. 2021. Detecting Depression in Thai Blog Posts: a Dataset and a Baseline. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 20–25, Online. Association for Computational Linguistics.
Cite (Informal):
Detecting Depression in Thai Blog Posts: a Dataset and a Baseline (Hämäläinen et al., WNUT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wnut-1.3.pdf