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Tweeting your mental health: Exploration of different classifiers and features with emotional signals in identifying mental health conditions

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conference contribution
posted on 19.11.2018 by Xuetong Chen, Martin Sykora, Tom Jackson, Suzanne Elayan, Fehmidah Munir
Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotion related analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotions as features and examined five popular machine learning classifiers in the task of identifying users with selfreported mental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based features and combined features showed promising improvements to the performance on this task.

History

School

  • Business and Economics

Department

  • Business

Published in

HICCS Hawaii International Conference on Computer Systems

Citation

CHEN, X. ... et al., 2018. Tweeting your mental health: Exploration of different classifiers and features with emotional signals in identifying mental health conditions. IN: Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS 2018) Hawaii, 2-6th January. Honolulu: Hawaii International Conference on System Sciences (HICSS), vol 8, pp. 5225-5233.

Publisher

© Hawaii International Conference on System Sciences (HICSS)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2018

Notes

This is an Open Access Article. It is published by HICCS under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0//

ISBN

9780998133119

Language

en

Location

Hawaii

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