Detecting Depression in Thai Blog Posts: a Dataset and a Baseline
This addresses the need for depression detection tools in Thai language, though it is incremental as it applies existing methods to a new dataset.
The researchers tackled the problem of detecting depression in Thai blog posts by creating the first openly available corpus for this task and achieved 77.53% accuracy using a Thai BERT model, establishing a baseline for future work.
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.