Detecting Mental Health Illness Using Short Comments

Takahiro Baba, Kensuke Baba, Daisuke Ikeda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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 languageEnglish
Title of host publicationAdvanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019
EditorsMakoto Takizawa, Fatos Xhafa, Leonard Barolli, Tomoya Enokido
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783030150310
Publication statusPublished - 2020
Event33rd International Conference on Advanced Information Networking and Applications, AINA-2019 - Matsue, Japan
Duration: Mar 27 2019Mar 29 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


Conference33rd International Conference on Advanced Information Networking and Applications, AINA-2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)


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