Semantic Color Mapping: A Pipeline for Assigning Meaningful Colors to Text

Abstract

Current visual text analytics applications do not regard color assignment as a prominent design consideration. We argue that there is a need for applying meaningful colors to text, enhancing comprehension and comparability. Hence, in this paper, we present a guideline to facilitate the choice of colors in text visualizations. The semantic color mapping pipeline is derived from literature and experiences in text visualization design and sums up design considerations, lessons learned, and best practices. The proposed pipeline starts by extracting labeled data from raw text, choosing an aggregation level to create an appropriate vector representation, then defining the unit of analysis to project the data into a low-dimensional space, and finally assigning colors based on the selected color space. We argue that applying such a pipeline enhances the understanding of attribute relations in text visualizations, as confirmed by two applications.