Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions

Skillicorn, D; Alsadhan, N; Billingsley, R; Williams, MA

Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions

Skillicorn, D Alsadhan, N Billingsley, R Williams, MA

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal, 2019, 11489 LNAI, pp. 29-41
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Skillicorn, D
dc.contributor.author	Alsadhan, N
dc.contributor.author	Billingsley, R
dc.contributor.author	Williams, MA
dc.date	2019-05-28
dc.date.accessioned	2020-06-12T07:37:16Z
dc.date.available	2020-06-12T07:37:16Z
dc.date.issued	2019-01-01
dc.identifier.citation	ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal, 2019, 11489 LNAI, pp. 29-41
dc.identifier.isbn	9783030183042
dc.identifier.issn	2255-2863
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/141389
dc.description.abstract	© 2019, Springer Nature Switzerland AG. Social robots and agents can interact with people better if they can infer their affective state (emotions). While they cannot yet recognise affective state from tone and body language, they can use the fragments of speech that they (over)hear. We show that emotions – as conventionally framed – are difficult to detect. We suggest, from empirical results, that this is because emotions are the wrong granularity; and that emotions contain subemotions that are much more clearly separated from one another, and so are both easier to detect and to exploit.
dc.language	en
dc.publisher	Springer
dc.relation	http://purl.org/au-research/grants/arc/DP160102693
dc.relation.ispartof	ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal
dc.relation.ispartof	Canadian Conference on Artificial Intelligence
dc.relation.isbasedon	10.1007/978-3-030-18305-9_3
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions
dc.type	Conference Proceeding
utslib.citation.volume	11489 LNAI
utslib.location.activity	Kingston, Canada
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-12T07:37:13Z
pubs.finish-date	2019-05-31
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-05-28
pubs.volume	11489 LNAI
dc.location	Switzerland

Abstract:

© 2019, Springer Nature Switzerland AG. Social robots and agents can interact with people better if they can infer their affective state (emotions). While they cannot yet recognise affective state from tone and body language, they can use the fragments of speech that they (over)hear. We show that emotions – as conventionally framed – are difficult to detect. We suggest, from empirical results, that this is because emotions are the wrong granularity; and that emotions contain subemotions that are much more clearly separated from one another, and so are both easier to detect and to exploit.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141389