Colour Coded Emotion Classification in Mental Health Social Media

Neil Vaughan; Maurice Mulvenna; RR Bond

doi:10.14236/ewic/HCI2018.172

Colour Coded Emotion Classification in Mental Health Social Media

Neil Vaughan, Maurice Mulvenna, RR Bond

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

170 Downloads (Pure)

Abstract

This 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.

Original language	English
Title of host publication	Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018)
Editors	Raymond Bond, Maurice Mulvenna, Jonathan Wallace, Michaela Black
Place of Publication	Swindon, UK
Publisher	BCS Learning & Development Ltd
Number of pages	5
DOIs	https://doi.org/10.14236/ewic/HCI2018.172
Publication status	Published (in print/issue) - 10 May 2018
Event	British HCI Conference 2018 - Belfast, Belfast, Northern Ireland Duration: 2 Jul 2018 → 6 Jul 2018

Conference

Conference	British HCI Conference 2018
Abbreviated title	BHCI2018
Country/Territory	Northern Ireland
City	Belfast
Period	2/07/18 → 6/07/18

Keywords

Affective computing
emotion
mental health
social media

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.14236/ewic/HCI2018.172

BHCI-2018_paper_254Author Accepted Manuscript, 193 KB

Cite this

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title = "Colour Coded Emotion Classification in Mental Health Social Media",

abstract = "This 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.",

keywords = "Affective computing, emotion, mental health, social media",

author = "Neil Vaughan and Maurice Mulvenna and RR Bond",

year = "2018",

month = may,

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doi = "10.14236/ewic/HCI2018.172",

language = "English",

editor = "Raymond Bond and Maurice Mulvenna and Jonathan Wallace and Michaela Black",

booktitle = "Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018)",

publisher = "BCS Learning & Development Ltd",

note = "British HCI Conference 2018, BHCI2018 ; Conference date: 02-07-2018 Through 06-07-2018",

}

Vaughan, N, Mulvenna, M & Bond, RR 2018, Colour Coded Emotion Classification in Mental Health Social Media. in R Bond, M Mulvenna, J Wallace & M Black (eds), Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). BCS Learning & Development Ltd, Swindon, UK, British HCI Conference 2018, Belfast, Northern Ireland, 2/07/18. https://doi.org/10.14236/ewic/HCI2018.172

Colour Coded Emotion Classification in Mental Health Social Media. / Vaughan, Neil; Mulvenna, Maurice ; Bond, RR.
Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). ed. / Raymond Bond; Maurice Mulvenna; Jonathan Wallace; Michaela Black. Swindon, UK: BCS Learning & Development Ltd, 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Colour Coded Emotion Classification in Mental Health Social Media

AU - Vaughan, Neil

AU - Mulvenna, Maurice

AU - Bond, RR

PY - 2018/5/10

Y1 - 2018/5/10

N2 - This 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.

AB - This 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.

KW - Affective computing

KW - emotion

KW - mental health

KW - social media

UR - https://dl.acm.org/citation.cfm?id=3301191

UR - https://ewic.bcs.org/upload/pdf/ewic_hci18_wip_paper82.pdf

U2 - 10.14236/ewic/HCI2018.172

DO - 10.14236/ewic/HCI2018.172

M3 - Conference contribution

BT - Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018)

A2 - Bond, Raymond

A2 - Mulvenna, Maurice

A2 - Wallace, Jonathan

A2 - Black, Michaela

PB - BCS Learning & Development Ltd

CY - Swindon, UK

T2 - British HCI Conference 2018

Y2 - 2 July 2018 through 6 July 2018

ER -

Colour Coded Emotion Classification in Mental Health Social Media

Abstract

Conference

Keywords

UN SDGs

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