Semantic Color Mapping: A Pipeline for Assigning Meaningful Colors to Text
METADATA ONLY
Loading...
Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
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.
Permanent link
Publication status
published
Editor
Book title
2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides)
Journal / series
Volume
Pages / Article No.
16 - 22
Publisher
IEEE
Event
4th IEEE Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Visualization; Image color analysis; Pipelines; Semantics; Data visualization; Libraries