The official code and data of the EMNLP2023 paper "Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts" (https://aclanthology.org/2023.emnlp-main.562/)
First, select_posts.py select risky posts for further MDD with symptom-based methods. The selected posts are in processed/symptom_sum_top16, which can be provided upon request, please contact chensiyuan925@sjtu.edu.cn or blmoistawinde@qq.com.
Next, bash runme_psyex.sh to run the experiments of PsyEx in both binary and multi-label setting.
Other files:
- data.py: defines the datasets and data module
- model.py: defines the models, including PsyEx and PsyEx without disease-specific attention layers.
- main_hier_clf.py: run experiments with PsyEx models
- runme_psyex_wo_multitask.sh: run the experiment of PsyEx without multi-task learning
- runme_psyex_wo_symp.sh: run the experiment of PsyEx without symptom stream
- runme_psyex_wo_multiattn: run the experiment of PsyEx without multiple attention layers for each disease.
Please don't hesitate to reach out if you encounter any issues with the code or dataset.
If you find the code and data useful, please cite our paper, and maybe you can give us a star ✨ on github.
@inproceedings{chen-etal-2023-detection,
title = "Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts",
author = "Chen, Siyuan and
Zhang, Zhiling and
Wu, Mengyue and
Zhu, Kenny",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.562",
pages = "9071--9084",
}