TY - GEN
T1 - Detecting Mental Health Illness Using Short Comments
AU - Baba, Takahiro
AU - Baba, Kensuke
AU - Ikeda, Daisuke
N1 - Funding Information:
supported by JSPS KAKENHI Grant Number
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85064015026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064015026&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15032-7_23
DO - 10.1007/978-3-030-15032-7_23
M3 - Conference contribution
AN - SCOPUS:85064015026
SN - 9783030150310
T3 - Advances in Intelligent Systems and Computing
SP - 265
EP - 271
BT - Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019
A2 - Takizawa, Makoto
A2 - Xhafa, Fatos
A2 - Barolli, Leonard
A2 - Enokido, Tomoya
PB - Springer Verlag
T2 - 33rd International Conference on Advanced Information Networking and Applications, AINA-2019
Y2 - 27 March 2019 through 29 March 2019
ER -
