SEACrowd/thai_depression

Tasks

• emotion-classification

Citation text from the card

@inproceedings{hamalainen-etal-2021-detecting,
    title = "Detecting Depression in Thai Blog Posts: a Dataset and a Baseline",
    author = {H{"a}m{"a}l{"a}inen, Mika  and
      Patpong, Pattama  and
      Alnajjar, Khalid  and
      Partanen, Niko  and
      Rueter, Jack},
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.wnut-1.3",
    doi = "10.18653/v1/2021.wnut-1.3",
    pages = "20--25",
    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.",
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia an

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