Suicidal ideation and mental disorder detection with attentive relation networks

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classify...

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Main Authors: Ji, Shaoxiong, Li, Xue, Huang, Zi, Cambria, Erik
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160703
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1607032022-08-01T05:30:30Z Suicidal ideation and mental disorder detection with attentive relation networks Ji, Shaoxiong Li, Xue Huang, Zi Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Suicidal Ideation Mental Disorder Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts. This research is supported by the Australian Research Council (ARC) Linkage Project (LP150100671). 2022-08-01T05:30:30Z 2022-08-01T05:30:30Z 2022 Journal Article Ji, S., Li, X., Huang, Z. & Cambria, E. (2022). Suicidal ideation and mental disorder detection with attentive relation networks. Neural Computing and Applications, 34(13), 10309-10319. https://dx.doi.org/10.1007/s00521-021-06208-y 0941-0643 https://hdl.handle.net/10356/160703 10.1007/s00521-021-06208-y 2-s2.0-85108649190 13 34 10309 10319 en Neural Computing and Applications © 2021 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Suicidal Ideation
Mental Disorder
spellingShingle Engineering::Computer science and engineering
Suicidal Ideation
Mental Disorder
Ji, Shaoxiong
Li, Xue
Huang, Zi
Cambria, Erik
Suicidal ideation and mental disorder detection with attentive relation networks
description Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Ji, Shaoxiong
Li, Xue
Huang, Zi
Cambria, Erik
format Article
author Ji, Shaoxiong
Li, Xue
Huang, Zi
Cambria, Erik
author_sort Ji, Shaoxiong
title Suicidal ideation and mental disorder detection with attentive relation networks
title_short Suicidal ideation and mental disorder detection with attentive relation networks
title_full Suicidal ideation and mental disorder detection with attentive relation networks
title_fullStr Suicidal ideation and mental disorder detection with attentive relation networks
title_full_unstemmed Suicidal ideation and mental disorder detection with attentive relation networks
title_sort suicidal ideation and mental disorder detection with attentive relation networks
publishDate 2022
url https://hdl.handle.net/10356/160703
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