The Color of the Year was first introduced by Pantone in 2000, and recently (the last decade) we saw the trend of introducing a Color of the Year being picked up by more and more companies. Paints and coatings companies typically select their colors of the year by extensive research by designers and trend experts, resulting in a plurality of colors being introduced as Color of the Year,

An important aspect of colour forecasting is the process of generating colour palettes to represent collections at fashion shows. Humans have traditionally done this manually, and can do it well, but there are often too many images and it becomes an unmanageable task. In this paper, automatic machine-learning methods are developed to generate colour palettes for a fashion show based on the runway images. A set of ground-truth data to test the models was constructed based on asking each of 22 participants to select three colours to represent each of the 48 images from a particular fashion show. A close agreement was shown between these data and the colours automatically generated using a model that incorporated both supervised and unsupervised machine learning. The work could be extended to analyse millions of images from social media feeds to provide data-driven insights for colour forecasting.

This study explores the temporal changes in sentiment associated with eight color names over an 18-month period at four observation points. We focus on the valence aspect of sentiment. We collected four datasets, each separated by 6 months, and each containing 18 000 mentions of each of the eight color names in English from Twitter users around the world. We calculated the weighted average sentiment score of each instance when a color is mentioned.We find that purple and pink are the most positive in average sentiment score in all observation points, whereas brown, red, and orange are ranked as the lowest in average sentiment score. In terms of relative rank in sentiment value associated with the color names, we find the three consecutive datasets of July 2020, January 2021 and July 2021 are more consistent with one another, while the January 2022 dataset is more different from the earlier three datasets. This finding indicates that the temporal consistency in color-associated sentiment might maintain within 1 year, while evolve and show more difference in a longer timeline. This study is useful to marketing professionals by revealing that color names are associated with sentiment and that these associations can be monitored using social media data regularly. We suggest that marketers can use our method to analyse the color-associated sentiment of color names regularly, maybe on an annual basis, in order to choose color names wisely.