Extrapolating continuous color emotions through deep learning
This work addresses color-emotion mapping for applications in design and psychology, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled the problem of predicting emotions associated with colors using deep learning on an experimental dataset, finding that males associate emotions with darker colors and females with brighter colors, with similar trends for age groups.
By means of an experimental dataset, we use deep learning to implement an RGB 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 typically associate a given emotion with darker colors while females 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.