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ID 118262
著者
康, 鑫 University of Tokushima|Tongji University 徳島大学 教育研究者総覧
Quan, Changqin Kobe University
キーワード
Suicide risk prediction
accumulated emotional traits
emotion accumulation
emotion covariance
emotion transition
資料タイプ
学術雑誌論文
抄録
Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limits the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people’s daily writings (i.e. Blogs), and examining these emotional traits which are predictive of suicidal behaviors. A complex emotion topic (CET) model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i.e., accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and non-suicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors.
掲載誌名
IEEE Journal of Biomedical and Health Informatics
ISSN
21682194
21682208
cat書誌ID
AA12720964
出版者
IEEE
20
5
開始ページ
1384
終了ページ
1396
発行日
2015-07-22
権利情報
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
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著者版
部局
理工学系