Extrapolating Continuous Color Emotions Through Deep Learning
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
American Physical Society
Open Access Color
GOLD
Green Open Access
Yes
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Publicly Funded
No
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 (type-f 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. © 2020 authors. Published by the American Physical Society.
Description
Keywords
[No Keyword Available], FOS: Computer and information sciences, Computer and Information Sciences, Computer Science - Machine Learning, QC1-999, FOS: Physical sciences, Associations, Quantitative Biology - Quantitative Methods, Machine Learning (cs.LG), Age, colour; emotion; machine learning; neural network, Biological Physics, Preferences, Quantitative Methods, Physics - Biological Physics, Quantitative Methods (q-bio.QM), Physics, Deep learning, Neural network, Quantitative Biology, Biological Physics (physics.bio-ph), FOS: Biological sciences, Computer Science, Natural Sciences
Turkish CoHE Thesis Center URL
Fields of Science
0501 psychology and cognitive sciences, 05 social sciences
Citation
WoS Q
Q1
Scopus Q
Q1

OpenCitations Citation Count
9
Source
Physical Review Research
Volume
2
Issue
3
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Scopus : 15
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1.66493898
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