- Python 3
- CPU or NVIDIA GPU + CUDA CuDNN
-
Install PyTorch and other dependencies.
please run the command
pip install -r requirements.txt.
Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. To address the issue, the authors of the DEPTWEET dataset will provide the dataset freely of charge to researchers in order to promote mental health research. Dataset can be found in this repo.
- Go to the
Scriptsdirectory:
cd Scripts- Place the dataset in the
../Dataset/folder and run the command:
python dataset.py- Go to the
Scriptsdirectory:
cd Scripts- Train stand-alone model (BERT-base-uncased) with GPU support:
python train.py- Train stand-alone model (BERT-base-uncased) with CPU only:
python train.py --device CPU- Train stand-alone SVM or LSTM model:
Codes are available at ./Notebooks folder.Information regarding other training parameters can be found at Scripts/common.py file.
Fine-tuned models will be saved at ../Models/ folder.
Evaluation output files will be saved at ../Output/ folder.
Figures will be saved at ../Figures/ folder.
If you use this code for your research, please cite our paper.
@article{KABIR2022107503,
title = {DEPTWEET: A typology for social media texts to detect depression severities},
journal = {Computers in Human Behavior},
pages = {107503},
year = {2022},
issn = {0747-5632},
doi = {https://doi.org/10.1016/j.chb.2022.107503},
url = {https://www.sciencedirect.com/science/article/pii/S0747563222003235},
author = {Mohsinul Kabir and Tasnim Ahmed and Md. Bakhtiar Hasan and Md Tahmid Rahman Laskar and Tarun Kumar Joarder and Hasan Mahmud and Kamrul Hasan},
keywords = {Social media, Mental health, Depression severity, Dataset},
abstract = {Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as ‘non-depressed’ or ‘depressed’. Moreover, three severity levels are considered for ‘depressed’ tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing summary statistics while setting strong baseline results using attention-based models like BERT and DistilBERT. Finally, we extensively address the limitations of the study to provide directions for further research.}
}