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

WNUT (ACL) 2021 · Mika Hämäläinen, Pattama Patpong, Khalid Alnajjar, Niko Partanen, Jack Rueter ·

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.

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Adam • Attention Dropout • BERT • Dense Connections • Dropout • GELU • Layer Normalization • Linear Layer • Linear Warmup With Linear Decay • LSTM • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Sigmoid Activation • Softmax • Tanh Activation • Weight Decay • WordPiece

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

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