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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Impulse
Top 10%
Influence
Average
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Top 10%

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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

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Fields of Science

0501 psychology and cognitive sciences, 05 social sciences

Citation

WoS Q

Q1

Scopus Q

Q1
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OpenCitations Citation Count
9

Source

Physical Review Research

Volume

2

Issue

3

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End Page

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Scopus : 15

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Mendeley Readers : 18

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1.66493898

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