Predicting affective connotation of visualizations from their constituent colors

2026-08-10

AI Summary

This study investigates how the emotional connotations of colors in visualizations can be leveraged to predict the overall affective impression of a chart. The authors propose models based on mean and weighted mean aggregation of color emotion scores, incorporating the proportional area coverage of each color. Evaluated across multiple chart types—including heatmaps, scatter plots, and bar charts—the approach demonstrates that color emotions exhibit both additivity and data dependency. Specifically, when color distributions are uniform, a simple mean model suffices; under skewed distributions, the weighted mean yields superior predictive performance. This work provides the first systematic evidence of these properties and establishes an interpretable, quantifiable framework for affective computing in data visualization.

Abstract

With increasing evidence that affective connotation (emotional association) is an important aspect of visual communication, there is a need for methods to predict affective connotation of visualizations. Many aspects of visualization design, including colors, textures, and shapes, can contribute to affective connotation, and a key question is how multiple design properties combine to determine the emotion association of a whole visualization. In this study, we focused specifically on color and tested whether it is possible to predict the affective connotation of whole visualizations by aggregating the emotion associations of the individual, constituent colors (additivity hypothesis). We also tested whether accounting for the size of colored regions, as determined by the underlying dataset, improved predictions (data-dependence hypothesis). We found that for colormap data visualizations in which colors were well-distributed across all colors in the color scale, the mean estimated associations of individual colors effectively predicted emotional associations of the maps as a whole (additivity; Exp. 1). For colormaps whose underlying datasets were biased to map more to colors at one end of the color scale, emotional associations were better predicted by a weighted mean that accounted for color frequency in the colormap (data-dependence; Exp. 2). Effects of additivity and data-dependence generalized to dot plots and bar charts (Exp. 3). These results suggest it is viable to predict affective connotation of whole visualizations from their individual design components, which has important implications for automating affective visualization design to support visual communication.

Problem

• affective connotation
• visualization
• color
• emotion association
• visual communication

Innovation

• affective connotation
• additivity
• data-dependence
• color aggregation
• emotion prediction