2021
EMNLP
EMNLP 2021
Detecting Depression in Thai Blog Posts: a Dataset and a Baseline
Abstract
AbstractWe 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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Interdisciplinary Bridge
— Deep Learning and Healthcare & Medicine and Machine Learning and Natural Language Processing
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Trend Setter
— Text Classification
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Keyword Pioneer
— thai bert
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Hot Topic Early Bird
— depression detection
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Large Language Models
Healthcare & Medicine > Clinical > Mental Health
Deep Learning > Models > Transformers
Natural Language Processing > Understanding > Text Classification
Machine Learning > Learning Types > Text Classification