Colour Coded Emotion Classification in Mental Health Social Media

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.

Content

Author and article information

Contributors

Neil Vaughan

Maurice Mulvenna

Raymond Bond

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-5

Affiliations

[0001]University of Chester, Thornton Science Park, Pool Ln, Chester, CH2 4NU

[0002]Ulster University

Shore Road, Newtownabbey, Co.

Antrim, BT37 0QB

Article

DOI: 10.14236/ewic/HCI2018.172

SO-VID: 22dc3cd9-77d4-4d67-aceb-a6be5af92831

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.172

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

C. O. AlmD. RothR. Sproat 2005 Emotions from text: machine learning for text-based emotion prediction Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT ’05 579 586
E. O. BantumN. ElhadadJ. E. OwenS. ZhangM. GolantJ. BuzagloJ. Giese-Davis 2017 Machine Learning for Identifying Emotional Expression in Text: Improving the Accuracy of Established Methods Journal of Technology in Behavioral Science 2 1 21 27
F. BurkhardtJ. Stegmann 2009 Emotional speech synthesis: Applications, history, and possible future Proc. ESSV
E. CambriaB. White 2014 May Jumping NLP Curves: A Review of Natural Language Processing Research IEEE Computational Intelligence Magazine 9 2 48 57
N. Chomsky 1957 Logical Structures In Language Journal of the Association for Information Science 8 4 284 2910
B. DesmetV. Hoste 2013 Emotion detection in suicide notes Expert Systems with Applications 40 16 6351 6358
EU Green Paper 2005 Improving the Mental Health of the Population. Towards a Strategy on Mental Health for the European Union World Health Organization Regional Office for Europe
R HarveyA MunceyN Vaughan 2018 Associating Colours with Emotions Detected in Social Media Tweets Symposium for Emotion Modelling and Detection in Social Media and Online Interaction, Proceedings of Artificial Intelligence and Simulation of Behaviour
J. HirschbergC. D. Manning 2015 Advances in Natural Language Processing Science 349 6245 261 266
R. Kohavi 1995 Wrappers for performance enhancement and oblivious decision graphs institutionCarnegie-mellon University Pittsburgh PA
W. G. LehnertM. H. Ringle 1982 Strategies for natural language processing New York Psychology Press
LiuB 2010 Sentiment analysis and subjectivity, Handbook of natural language processing 2nd 2010
B. Manav 2017 Color-emotion associations, designing color schemes for urban environment-architectural settings Color research and application 42 5 631 640
T. MikolovS. KombrinkL. BurgetJ. CernockyS. Khudanpur 2011 Extensions of recurrent neural network language model Acoustics, Speech and Signal Processing (ICASSP) 5528
C. B. MoonH. KimD. W. LeeB. M. Kim 2013 Mood lighting system reflecting music mood Color Research & Application 40 2 201
L.-C. OuR. LuoA. WookcockA. WrightL. C. OuM. R. LuoA. WoodcockA. Wright 2004 2004 A study of colour emotion and colour preference. Part I: Colour emotions for single colours Color Research & Application 39 3 232 240
J PestianH NasrallahP MatykiewiczA BennettA Leenaars 2010 Suicide note classification using natural language processing: A content analysis Journal of Biomedical Informatics Insights 3 19 28
D. PizziF. CharlesJ.-L. Lugrin, & M. Cavazza 2007 Interactive Storytelling with Literary Feelings Affective Computing and Intelligent Interaction 630 641
Talklife 2018 http://talklife.co/, retrieved May 2018

Comments

Comment on this article

[1] C. O. AlmD. RothR. Sproat 2005 Emotions from text: machine learning for text-based emotion prediction Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT ’05 579 586

[2] E. O. BantumN. ElhadadJ. E. OwenS. ZhangM. GolantJ. BuzagloJ. Giese-Davis 2017 Machine Learning for Identifying Emotional Expression in Text: Improving the Accuracy of Established Methods Journal of Technology in Behavioral Science 2 1 21 27

[3] F. BurkhardtJ. Stegmann 2009 Emotional speech synthesis: Applications, history, and possible future Proc. ESSV

[4] E. CambriaB. White 2014 May Jumping NLP Curves: A Review of Natural Language Processing Research IEEE Computational Intelligence Magazine 9 2 48 57

[5] N. Chomsky 1957 Logical Structures In Language Journal of the Association for Information Science 8 4 284 2910

[6] B. DesmetV. Hoste 2013 Emotion detection in suicide notes Expert Systems with Applications 40 16 6351 6358

[7] EU Green Paper 2005 Improving the Mental Health of the Population. Towards a Strategy on Mental Health for the European Union World Health Organization Regional Office for Europe

[8] R HarveyA MunceyN Vaughan 2018 Associating Colours with Emotions Detected in Social Media Tweets Symposium for Emotion Modelling and Detection in Social Media and Online Interaction, Proceedings of Artificial Intelligence and Simulation of Behaviour

[9] J. HirschbergC. D. Manning 2015 Advances in Natural Language Processing Science 349 6245 261 266

[10] R. Kohavi 1995 Wrappers for performance enhancement and oblivious decision graphs institutionCarnegie-mellon University Pittsburgh PA

[11] W. G. LehnertM. H. Ringle 1982 Strategies for natural language processing New York Psychology Press

[12] LiuB 2010 Sentiment analysis and subjectivity, Handbook of natural language processing 2nd 2010

[13] B. Manav 2017 Color-emotion associations, designing color schemes for urban environment-architectural settings Color research and application 42 5 631 640

[14] T. MikolovS. KombrinkL. BurgetJ. CernockyS. Khudanpur 2011 Extensions of recurrent neural network language model Acoustics, Speech and Signal Processing (ICASSP) 5528

[15] C. B. MoonH. KimD. W. LeeB. M. Kim 2013 Mood lighting system reflecting music mood Color Research & Application 40 2 201

[16] L.-C. OuR. LuoA. WookcockA. WrightL. C. OuM. R. LuoA. WoodcockA. Wright 2004 2004 A study of colour emotion and colour preference. Part I: Colour emotions for single colours Color Research & Application 39 3 232 240

[17] J PestianH NasrallahP MatykiewiczA BennettA Leenaars 2010 Suicide note classification using natural language processing: A content analysis Journal of Biomedical Informatics Insights 3 19 28

[18] D. PizziF. CharlesJ.-L. Lugrin, & M. Cavazza 2007 Interactive Storytelling with Literary Feelings Affective Computing and Intelligent Interaction 630 641

[19] Talklife 2018 http://talklife.co/, retrieved May 2018

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