Extrapolating Continuous Color Emotions Through Deep Learning

dc.contributor.author Ram, Vishaal
dc.contributor.author Schaposnik, Laura P.
dc.contributor.author Konstantinou, Nikos
dc.contributor.author Volkan, Eliz
dc.contributor.author Papadatou-Pastou, Marietta
dc.contributor.author Manav, Banu
dc.contributor.author Jonauskaite, Domicele
dc.contributor.author Mohr, Christine
dc.date.accessioned 2021-01-28T13:03:56Z
dc.date.available 2021-01-28T13:03:56Z
dc.date.issued 2020
dc.description.abstract By means of an experimental dataset, we use deep learning to implement an RGB (red, green, and blue) extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males (type-m individuals) typically associate a given emotion with darker colors, while females (typef individuals) associate it with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors. en_US
dc.description.sponsorship National Science Foundation (NSF) Swiss National Science Foundation (SNSF) en_US
dc.identifier.doi 10.1103/PhysRevResearch.2.033350 en_US
dc.identifier.issn 2643-1564 en_US
dc.identifier.issn 2643-1564
dc.identifier.scopus 2-s2.0-85113520218 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/3789
dc.identifier.uri https://doi.org/10.1103/PhysRevResearch.2.033350
dc.language.iso en en_US
dc.publisher Amer Physical Soc en_US
dc.relation.ispartof Physical Review Research
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Associations en_US
dc.subject Preferences en_US
dc.subject Age en_US
dc.title Extrapolating Continuous Color Emotions Through Deep Learning en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Manav, Banu en_US
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gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department Fakülteler, Sanat ve Tasarım Fakültesi, İç Mimarlık ve Çevre Tasarımı Bölümü en_US
gdc.description.issue 3 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.volume 2 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W3082845913
gdc.identifier.wos WOS:000604171000005 en_US
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gdc.oaire.keywords FOS: Computer and information sciences
gdc.oaire.keywords Computer and Information Sciences
gdc.oaire.keywords Computer Science - Machine Learning
gdc.oaire.keywords QC1-999
gdc.oaire.keywords FOS: Physical sciences
gdc.oaire.keywords Associations
gdc.oaire.keywords Quantitative Biology - Quantitative Methods
gdc.oaire.keywords Machine Learning (cs.LG)
gdc.oaire.keywords Age
gdc.oaire.keywords colour; emotion; machine learning; neural network
gdc.oaire.keywords Biological Physics
gdc.oaire.keywords Preferences
gdc.oaire.keywords Quantitative Methods
gdc.oaire.keywords Physics - Biological Physics
gdc.oaire.keywords Quantitative Methods (q-bio.QM)
gdc.oaire.keywords Physics
gdc.oaire.keywords Deep learning
gdc.oaire.keywords Neural network
gdc.oaire.keywords Quantitative Biology
gdc.oaire.keywords Biological Physics (physics.bio-ph)
gdc.oaire.keywords FOS: Biological sciences
gdc.oaire.keywords Computer Science
gdc.oaire.keywords Natural Sciences
gdc.oaire.popularity 7.2990725E-9
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gdc.oaire.sciencefields 0501 psychology and cognitive sciences
gdc.oaire.sciencefields 05 social sciences
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gdc.opencitations.count 9
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gdc.relation.journal Physical Review Research
gdc.scopus.citedcount 15
gdc.virtual.author Manav, Banu
gdc.wos.citedcount 13
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