@inproceedings{thannickal-etal-2023-techssn4,
title = "{T}ech{SSN}4@{LT}-{EDI}-2023: Depression Sign Detection in Social Media Postings using {D}istil{BERT} Model",
author = "Thannickal, Krupa Elizabeth and
P, Sanmati and
Sivanaiah, Rajalakshmi and
S, Angel Deborah",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.36",
pages = "239--243",
abstract = "As world population increases, more people are living to the age when depression or Major Depressive Disorder (MDD) commonly occurs. Consequently, the number of those who suffer from such disorders is rising. There is a pressing need for faster and reliable diagnosis methods. This paper proposes the method to analyse text input from social media posts of subjects to determine the severity class of depression. We have used the DistilBERT transformer to process these texts and classify the individuals across three severity labels - {`}not depression{'}, {`}moderate{'} and {`}severe{'}. The results showed the macro F1-score of 0.437 when the model was trained for 5 epochs with a comparative performance across the labels.The team acquired 6th rank while the top team scored macro F1-score as 0.470. We hope that this system will support further research into the early identification of depression in individuals to promote effective medical research and related treatments.",
}
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%0 Conference Proceedings
%T TechSSN4@LT-EDI-2023: Depression Sign Detection in Social Media Postings using DistilBERT Model
%A Thannickal, Krupa Elizabeth
%A P, Sanmati
%A Sivanaiah, Rajalakshmi
%A S, Angel Deborah
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F thannickal-etal-2023-techssn4
%X As world population increases, more people are living to the age when depression or Major Depressive Disorder (MDD) commonly occurs. Consequently, the number of those who suffer from such disorders is rising. There is a pressing need for faster and reliable diagnosis methods. This paper proposes the method to analyse text input from social media posts of subjects to determine the severity class of depression. We have used the DistilBERT transformer to process these texts and classify the individuals across three severity labels - ‘not depression’, ‘moderate’ and ‘severe’. The results showed the macro F1-score of 0.437 when the model was trained for 5 epochs with a comparative performance across the labels.The team acquired 6th rank while the top team scored macro F1-score as 0.470. We hope that this system will support further research into the early identification of depression in individuals to promote effective medical research and related treatments.
%U https://aclanthology.org/2023.ltedi-1.36
%P 239-243
Markdown (Informal)
[TechSSN4@LT-EDI-2023: Depression Sign Detection in Social Media Postings using DistilBERT Model](https://aclanthology.org/2023.ltedi-1.36) (Thannickal et al., LTEDI-WS 2023)
ACL