Colour Coded Emotion Classification in Mental Health Social Media

Neil Vaughan, Maurice Mulvenna, RR Bond

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
LanguageEnglish
Title of host publication Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018)
EditorsRaymond Bond, Maurice Mulvenna, Jonathan Wallace, Michaela Black
Place of PublicationSwindon, UK
Number of pages5
DOIs
Publication statusPublished - 10 May 2018
EventBritish HCI Conference 2018 - Belfast, Belfast, Northern Ireland
Duration: 2 Jul 20186 Jul 2018

Conference

ConferenceBritish HCI Conference 2018
Abbreviated titleBHCI2018
CountryNorthern Ireland
CityBelfast
Period2/07/186/07/18

Fingerprint

Health
Color
Human computer interaction
Medical problems
User interfaces

Keywords

  • Affective computing
  • emotion
  • mental health
  • social media

Cite this

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) Swindon, UK. https://doi.org/10.14236/ewic/HCI2018.172
Vaughan, Neil ; Mulvenna, Maurice ; Bond, RR. / Colour Coded Emotion Classification in Mental Health Social Media. Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). editor / Raymond Bond ; Maurice Mulvenna ; Jonathan Wallace ; Michaela Black. Swindon, UK, 2018.
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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). 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, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.

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Vaughan N, Mulvenna M, Bond RR. Colour Coded Emotion Classification in Mental Health Social Media. In Bond R, Mulvenna M, Wallace J, Black M, editors, Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). Swindon, UK. 2018 https://doi.org/10.14236/ewic/HCI2018.172