Paralinguistic recommendations for affective word clouds

Abstract

Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and color palettes are powerful paralinguistic signals that may determine this impact, but, typically, the expectation is that they are chosen by the users. We present an affect-aware font and color palette selection methodology that aims to facilitate more informed choices. We induce associations of fonts with a set of eight affects, and evaluate the resulting data in a series of user studies both on individual words as well as in word clouds. Relying on a recent study to procure affective color palettes, we carry out a similar user study to understand the impact of color choices on word clouds. Our findings suggest that both fonts and color palettes are powerful tools contributing to the affect associated with a word cloud. The experiments further confirm that the novel datasets we propose are successful in enabling this. Based on this data, we implement a prototype that allows users to specify a desired affect and recommends congruent fonts and color palettes for the word cloud.