Detecting Mental Health Illness Using Short Comments

Takahiro Baba; Kensuke Baba; Daisuke Ikeda

doi:10.1007/978-3-030-15032-7_23

Detecting Mental Health Illness Using Short Comments

Takahiro Baba, Kensuke Baba, Daisuke Ikeda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Mental health illness has become a serious public problem. Finding changes in everyday behavior is a demand. This paper tries to detect persons who have mental health illness using their short comments posted to social network systems. The novelty of this study is using comments in a system for communication between users with mental health illness, in order to prepare a sufficient amount of supervised data for machine learning. The authors used approximately 120,000 comments in the system as positive samples and 120,000 comments in Twitter as negative samples for detecting mental health illness. Both data are posted short comments on a daily basis. The authors conducted a straightforward classification of the comments using a support vector machine and surface-level features of the comments. The accuracy of the classification is 0.92 and the characteristic phrases used for the classification are related to troubles in mental health. The ability to classify everyday statements can be expected to lead to the early detection of mental disorders.

Original language	English
Title of host publication	Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019
Editors	Makoto Takizawa, Fatos Xhafa, Leonard Barolli, Tomoya Enokido
Publisher	Springer Verlag
Pages	265-271
Number of pages	7
ISBN (Print)	9783030150310
DOIs	https://doi.org/10.1007/978-3-030-15032-7_23
Publication status	Published - 2020
Externally published	Yes
Event	33rd International Conference on Advanced Information Networking and Applications, AINA-2019 - Matsue, Japan Duration: Mar 27 2019 → Mar 29 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	926
ISSN (Print)	2194-5357

Conference

Conference	33rd International Conference on Advanced Information Networking and Applications, AINA-2019
Country/Territory	Japan
City	Matsue
Period	3/27/19 → 3/29/19

Keywords

Data mining
Machine learning
Mental health illness
Social network system
Text processing

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-15032-7_23

Cite this

Baba, T., Baba, K., & Ikeda, D. (2020). Detecting Mental Health Illness Using Short Comments. In M. Takizawa, F. Xhafa, L. Barolli, & T. Enokido (Eds.), Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019 (pp. 265-271). (Advances in Intelligent Systems and Computing; Vol. 926). Springer Verlag. https://doi.org/10.1007/978-3-030-15032-7_23

Detecting Mental Health Illness Using Short Comments. / Baba, Takahiro; Baba, Kensuke; Ikeda, Daisuke.

Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019. ed. / Makoto Takizawa; Fatos Xhafa; Leonard Barolli; Tomoya Enokido. Springer Verlag, 2020. p. 265-271 (Advances in Intelligent Systems and Computing; Vol. 926).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baba, T, Baba, K & Ikeda, D 2020, Detecting Mental Health Illness Using Short Comments. in M Takizawa, F Xhafa, L Barolli & T Enokido (eds), Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019. Advances in Intelligent Systems and Computing, vol. 926, Springer Verlag, pp. 265-271, 33rd International Conference on Advanced Information Networking and Applications, AINA-2019, Matsue, Japan, 3/27/19. https://doi.org/10.1007/978-3-030-15032-7_23

Baba T, Baba K, Ikeda D. Detecting Mental Health Illness Using Short Comments. In Takizawa M, Xhafa F, Barolli L, Enokido T, editors, Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019. Springer Verlag. 2020. p. 265-271. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-15032-7_23

Baba, Takahiro ; Baba, Kensuke ; Ikeda, Daisuke. / Detecting Mental Health Illness Using Short Comments. Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019. editor / Makoto Takizawa ; Fatos Xhafa ; Leonard Barolli ; Tomoya Enokido. Springer Verlag, 2020. pp. 265-271 (Advances in Intelligent Systems and Computing).

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abstract = "Mental health illness has become a serious public problem. Finding changes in everyday behavior is a demand. This paper tries to detect persons who have mental health illness using their short comments posted to social network systems. The novelty of this study is using comments in a system for communication between users with mental health illness, in order to prepare a sufficient amount of supervised data for machine learning. The authors used approximately 120,000 comments in the system as positive samples and 120,000 comments in Twitter as negative samples for detecting mental health illness. Both data are posted short comments on a daily basis. The authors conducted a straightforward classification of the comments using a support vector machine and surface-level features of the comments. The accuracy of the classification is 0.92 and the characteristic phrases used for the classification are related to troubles in mental health. The ability to classify everyday statements can be expected to lead to the early detection of mental disorders.",

keywords = "Data mining, Machine learning, Mental health illness, Social network system, Text processing",

author = "Takahiro Baba and Kensuke Baba and Daisuke Ikeda",

note = "Funding Information: supported by JSPS KAKENHI Grant Number Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 33rd International Conference on Advanced Information Networking and Applications, AINA-2019 ; Conference date: 27-03-2019 Through 29-03-2019",

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AB - Mental health illness has become a serious public problem. Finding changes in everyday behavior is a demand. This paper tries to detect persons who have mental health illness using their short comments posted to social network systems. The novelty of this study is using comments in a system for communication between users with mental health illness, in order to prepare a sufficient amount of supervised data for machine learning. The authors used approximately 120,000 comments in the system as positive samples and 120,000 comments in Twitter as negative samples for detecting mental health illness. Both data are posted short comments on a daily basis. The authors conducted a straightforward classification of the comments using a support vector machine and surface-level features of the comments. The accuracy of the classification is 0.92 and the characteristic phrases used for the classification are related to troubles in mental health. The ability to classify everyday statements can be expected to lead to the early detection of mental disorders.

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ER -

Detecting Mental Health Illness Using Short Comments

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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