Detecting Mental Health Illness Using Short Comments

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)


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.


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



This work was supported by JSPS KAKENHI Grant Number 15H02787.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Kyushu UniversityFukuokaJapan
  2. 2.Fujitsu LaboratoriesKawasakiJapan

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