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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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. |
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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 |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Ji, Shaoxiong Li, Xue Huang, Zi Cambria, Erik |
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Article |
author |
Ji, Shaoxiong Li, Xue Huang, Zi Cambria, Erik |
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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 |
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2022 |
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https://hdl.handle.net/10356/160703 |
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