Mental Health Prediction through Text Chat Conversations

Nouman, M; Sara, H; Khoo, SY; Mahmud, MAP; Kouzani, AZ

Mental Health Prediction through Text Chat Conversations

Nouman, M Sara, H Khoo, SY Mahmud, MAP Kouzani, AZ

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 International Joint Conference on Neural Networks (IJCNN), 2023, 2023-June
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Nouman, M
dc.contributor.author	Sara, H
dc.contributor.author	Khoo, SY
dc.contributor.author	Mahmud, MAP
dc.contributor.author	Kouzani, AZ
dc.date	2023-06-18
dc.date.accessioned	2024-05-13T01:39:36Z
dc.date.available	2024-05-13T01:39:36Z
dc.date.issued	2023-01-01
dc.identifier.citation	2023 International Joint Conference on Neural Networks (IJCNN), 2023, 2023-June
dc.identifier.isbn	9781665488679
dc.identifier.issn	2161-4393
dc.identifier.uri	http://hdl.handle.net/10453/178864
dc.description.abstract	This study investigated the viability of classifying mental health conditions from text chat conversations using deep learning DL models A novel dataset of real time users was acquired from the Lyf Support app which was labeled by psychologists to conduct the experiments for users mental health prediction The findings suggest that DL models could be used to identify mental health conditions from text chat conversations According to evaluated results the bidirectional gated recurrent unit BiGRU outperformed other models by achieving over 83 accuracy The dataset employed in this study may be crucial for future research on online social media users mental health
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartof	2023 International Joint Conference on Neural Networks
dc.relation.ispartofseries	IEEE International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/ijcnn54540.2023.10191849
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Mental Health Prediction through Text Chat Conversations
dc.type	Conference Proceeding
utslib.citation.volume	2023-June
utslib.location.activity	Gold Coast, Australia
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T01:39:34Z
pubs.finish-date	2023-06-23
pubs.publication-status	Published
pubs.start-date	2023-06-18
pubs.volume	2023-June

Abstract:

This study investigated the viability of classifying mental health conditions from text chat conversations using deep learning DL models A novel dataset of real time users was acquired from the Lyf Support app which was labeled by psychologists to conduct the experiments for users mental health prediction The findings suggest that DL models could be used to identify mental health conditions from text chat conversations According to evaluated results the bidirectional gated recurrent unit BiGRU outperformed other models by achieving over 83 accuracy The dataset employed in this study may be crucial for future research on online social media users mental health

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http://hdl.handle.net/10453/178864