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Title: Tweeting your mental health: Exploration of different classifiers and features with emotional signals in identifying mental health conditions
Authors: Chen, Xuetong
Sykora, Martin D.
Jackson, Thomas
Elayan, Suzanne
Munir, Fehmidah
Issue Date: 2018
Publisher: © Hawaii International Conference on System Sciences (HICSS)
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.
Abstract: 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.
Description: 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//
Version: Published
DOI: 10.24251/HICSS.2018.421
URI: https://dspace.lboro.ac.uk/2134/36066
Publisher Link: http://doi.org/10.24251/HICSS.2018.421
ISBN: 9780998133119
Appears in Collections:Conference Papers and Presentations (Business)

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