Emosaic: Visualizing Affective Content of Text at Varying Granularity
Philipp Geuder, Marie Claire Leidinger, Martin von Lupin, Marian, D\"ork, Tobias Schr\"oder

TL;DR
Emosaic is a visualization tool that maps the emotional content of texts onto colors based on a three-dimensional affective model, enabling detailed and interactive exploration of emotional nuances in textual data.
Contribution
It introduces a novel color mapping based on valence, arousal, and dominance to visualize affective content at multiple semantic granularities in text.
Findings
Effective color mapping of affective space enhances emotional understanding.
Interactive features facilitate detailed exploration of text emotions.
Supports multi-level analysis from aggregate to detailed text segments.
Abstract
This paper presents Emosaic, a tool for visualizing the emotional tone of text documents, considering multiple dimensions of emotion and varying levels of semantic granularity. Emosaic is grounded in psychological research on the relationship between language, affect, and color perception. We capitalize on an established three-dimensional model of human emotion: valence (good, nice vs. bad, awful), arousal (calm, passive vs. exciting, active) and dominance (weak, controlled vs. strong, in control). Previously, multi-dimensional models of emotion have been used rarely in visualizations of textual data, due to the perceptual challenges involved. Furthermore, until recently most text visualizations remained at a high level, precluding closer engagement with the deep semantic content of the text. Informed by empirical studies, we introduce a color mapping that translates any point in…
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Taxonomy
TopicsColor perception and design · Advanced Text Analysis Techniques · Categorization, perception, and language
MethodsAffine Coupling · Normalizing Flows
