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dc.contributor.authorVaughan, Neil*
dc.contributor.authorMulvenna, Maurice*
dc.contributor.authorBond, Raymond*
dc.date.accessioned2019-03-04T14:35:39Z
dc.date.available2019-03-04T14:35:39Z
dc.date.issued2018-07-06
dc.identifier.citationVaughan, N., Mulvenna, M., & Bond, R. (2018). Colour Coded Emotion Classification in Mental Health Social Media. Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI 2018). Swindon, United Kingdom: BCS.en
dc.identifier.doi10.14236/ewic/HCI2018.172
dc.identifier.urihttp://hdl.handle.net/10034/621940
dc.description.abstractThis research applies emotion detection to messages from online mental health social media. In particular, this focusses on specialised social media for users to report health or mental health problems. Automatically detecting the emotion in social media can help to rapidly identify any concerning problems which could benefit from intervention aiming to prevent self-harming or suicide. Detecting emotions enables messages to be colour coordinated according to the emotion to enhance the human-computer interaction. A supervised classification method is applied to a labelled dataset and results presented. A prototype user interface system is developed based on detecting emotion, colour coding the message to display detected emotions to users in real-time.
dc.language.isoenen
dc.publisherBCS, The Chartered Institute for IT, ACM Proceedingsen
dc.relation.urlhttps://link.springer.com/chapter/10.1007/978-3-319-95972-6_55en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectEmotion detectionen
dc.subjectSocial Media Analysisen
dc.subjectText miningen
dc.titleColour Coded Emotion Classification in Mental Health Social Mediaen
dc.typeArticleen
dc.contributor.departmentRoyal Academy of Engineering; University of Chesteren
dc.date.accepted2018-05-30
or.grant.openaccessYesen
rioxxterms.funderRoyal Academy of Engineering - Dr Neil Vaughan Research Fellowshipen_US
rioxxterms.identifier.projectCSIS17/03en_US
rioxxterms.versionAMen
rioxxterms.licenseref.startdate2019-04-04


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