Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions

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dc.contributor.author Chen, Xuetong
dc.contributor.author Sykora, Martin
dc.contributor.author Jackson, Thomas
dc.contributor.author Elayan, Suzanne
dc.contributor.author Munir, Fehmidah
dc.date.accessioned 2017-12-28T01:50:37Z
dc.date.available 2017-12-28T01:50:37Z
dc.date.issued 2018-01-03
dc.description.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 self-reported 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.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2018.421
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50309
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Social Media and Healthcare Technology
dc.subject Emotion Analysis, Mental Health, Machine Learning, Social Media, Text Mining
dc.title Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions
dc.type Conference Paper
dc.type.dcmi Text
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