Quantity estimation in visualizations of tagged text
Michael A. Correll, Eric C. Alexander, Michael Gleicher
A valuable task in text visualization is to have viewers make judgments about text that has been annotated (either by hand or by some algorithm such as text clustering or entity extraction). In this work we look at the ability of viewers to make judgments about the relative quantities of tags in annotated text (specifically text tagged with one of a set of qualitatively distinct colors), and examine design choices that can improve performance at extracting statistical information from these texts. We find that viewers can efficiently and accurately estimate the proportions of tag levels over a range of situations; however accuracy can be improved through color choice and area adjustments.