Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT

This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 data...
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Autor*in:	Jaedong Oh [verfasserIn] Mirae Kim [verfasserIn] Hyejin Park [verfasserIn] Hayoung Oh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	depression intensity Best-Worst Scaling DSM-5 dataset DistilBERT attention

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 10, p 6223
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:10, p 6223

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app13106223

Katalog-ID:	DOAJ094423350

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language	English
source	In Applied Sciences 13(2023), 10, p 6223 volume:13 year:2023 number:10, p 6223
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topic_facet	depression intensity Best-Worst Scaling DSM-5 dataset DistilBERT attention Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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authorswithroles_txt_mv	Jaedong Oh @@aut@@ Mirae Kim @@aut@@ Hyejin Park @@aut@@ Hayoung Oh @@aut@@
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title	Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT
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abstract	This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 dataset containing nine types of depression annotations based on major depressive disorder (MDD) episodes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed model employs the DistilBERT model for both tasks and demonstrates superior performance compared to other machine learning and deep learning models. We suggest using our model for real-time depressive emotion detection tasks that demand speed and accuracy. Overall, the AuD model significantly advances the accurate detection of depressive emotions in user utterances.
abstractGer	This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 dataset containing nine types of depression annotations based on major depressive disorder (MDD) episodes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed model employs the DistilBERT model for both tasks and demonstrates superior performance compared to other machine learning and deep learning models. We suggest using our model for real-time depressive emotion detection tasks that demand speed and accuracy. Overall, the AuD model significantly advances the accurate detection of depressive emotions in user utterances.
abstract_unstemmed	This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 dataset containing nine types of depression annotations based on major depressive disorder (MDD) episodes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed model employs the DistilBERT model for both tasks and demonstrates superior performance compared to other machine learning and deep learning models. We suggest using our model for real-time depressive emotion detection tasks that demand speed and accuracy. Overall, the AuD model significantly advances the accurate detection of depressive emotions in user utterances.
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