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.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 | |
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| gdc.oaire.sciencefields | 0501 psychology and cognitive sciences | |
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| gdc.virtual.author | Manav, Banu | |
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