Please use this identifier to cite or link to this item:
Title: Suicidal ideation and mental disorder detection with attentive relation networks
Authors: Ji, Shaoxiong
Li, Xue
Huang, Zi
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: 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.
Journal: Neural Computing and Applications
Abstract: 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.
ISSN: 0941-0643
DOI: 10.1007/s00521-021-06208-y
Schools: School of Computer Science and Engineering 
Rights: © 2021 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Citations 20

Updated on Sep 23, 2023

Web of ScienceTM
Citations 20

Updated on Sep 25, 2023

Page view(s)

Updated on Sep 28, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.