@inproceedings{yenumulapalli-etal-2023-techssn1,
title = "{T}ech{SSN}1 at {LT}-{EDI}-2023: Depression Detection and Classification using {BERT} Model for Social Media Texts",
author = "Yenumulapalli, Venkatasai Ojus and
R, Vijai Aravindh 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-1.22",
pages = "149--154",
abstract = "Depression is a severe mental health disorder characterized by persistent feelings of sadness and anxiety, a decline in cognitive functioning resulting in drastic changes in a human{'}s psychological and physical well-being. However, depression is curable completely when treated at a suitable time and treatment resulting in the rejuvenation of an individual. The objective of this paper is to devise a technique for detecting signs of depression from English social media comments as well as classifying them based on their intensity into severe, moderate, and not depressed categories. The paper illustrates three approaches that are developed when working toward the problem. Of these approaches, the BERT model proved to be the most suitable model with an F1 macro score of 0.407, which gave us the 11th rank overall.",
}
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%0 Conference Proceedings
%T TechSSN1 at LT-EDI-2023: Depression Detection and Classification using BERT Model for Social Media Texts
%A Yenumulapalli, Venkatasai Ojus
%A R, Vijai Aravindh
%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 yenumulapalli-etal-2023-techssn1
%X Depression is a severe mental health disorder characterized by persistent feelings of sadness and anxiety, a decline in cognitive functioning resulting in drastic changes in a human’s psychological and physical well-being. However, depression is curable completely when treated at a suitable time and treatment resulting in the rejuvenation of an individual. The objective of this paper is to devise a technique for detecting signs of depression from English social media comments as well as classifying them based on their intensity into severe, moderate, and not depressed categories. The paper illustrates three approaches that are developed when working toward the problem. Of these approaches, the BERT model proved to be the most suitable model with an F1 macro score of 0.407, which gave us the 11th rank overall.
%U https://aclanthology.org/2023.ltedi-1.22
%P 149-154
Markdown (Informal)
[TechSSN1 at LT-EDI-2023: Depression Detection and Classification using BERT Model for Social Media Texts](https://aclanthology.org/2023.ltedi-1.22) (Yenumulapalli et al., LTEDI-WS 2023)
ACL