<div class="csl-bib-body">
<div class="csl-entry">Nalis, I., & Neidhardt, J. (2023). Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation. In <i>Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization</i> (pp. 325–330). Association for Computing Machinery. https://doi.org/10.1145/3563359.3596990</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192197
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dc.description.abstract
While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook the complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than static, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adaptation and personalization.
en
dc.description.sponsorship
Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.relation.ispartofseries
UMAP '23 Adjunct
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dc.subject
cognitive science
en
dc.subject
emotion detection
en
dc.subject
interdisciplinarity
en
dc.subject
sentiment analysis
en
dc.title
Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.publication
Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
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dc.relation.isbn
978-1-4503-9891-6
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dc.description.startpage
325
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dc.description.endpage
330
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dc.relation.grantno
CDL Neidhardt
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
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tuw.peerreviewed
true
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tuw.relation.publisher
Association for Computing Machinery
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tuw.relation.publisherplace
New York
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tuw.project.title
Christian Doppler Labor für Weiterentwicklung des State-of-the-Art von Recommender-Systemen in mehreren Domänen
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tuw.researchTopic.id
I4
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E194-04 - Forschungsbereich Data Science
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tuw.publisher.doi
10.1145/3563359.3596990
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dc.description.numberOfPages
6
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tuw.author.orcid
0000-0001-7101-3229
-
tuw.author.orcid
0000-0001-7184-1841
-
tuw.event.name
UMAP '23: 31st ACM Conference on User Modeling, Adaptation and Personalization
en
tuw.event.startdate
26-06-2023
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tuw.event.enddate
26-06-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Limassol
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tuw.event.country
CY
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tuw.event.presenter
Nalis, Irina
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tuw.event.track
Multi Track
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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item.languageiso639-1
en
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item.openairetype
conference paper
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item.grantfulltext
none
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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crisitem.author.dept
E194-04 - Forschungsbereich Data Science
-
crisitem.author.dept
E194-04 - Forschungsbereich Data Science
-
crisitem.author.orcid
0000-0001-7184-1841
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering