@techreport{GeuderLeidingerLupinetal., type = {Working Paper}, author = {Geuder, Philipp and Leidinger, Marie-Claire and Lupin, Martin von and D{\"o}rk, Marian and Schr{\"o}der, Tobias}, title = {Emosaic: Visualizing Affective Content of Text at Varying Granularity}, series = {arxiv.org}, journal = {arxiv.org}, publisher = {Cornell University}, doi = {10.48550/arXiv.2002.10096}, pages = {9}, 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 three-dimensional affective space into a unique color. Emosaic uses affective dictionaries of words annotated with the three emotional parameters of the valence-arousal-dominance model to extract emotional meanings from texts and then assigns to them corresponding color parameters of the hue-saturation-brightness color space. This approach of mapping emotion to color is aimed at helping readers to more easily grasp the emotional tone of the text. Several features of Emosaic allow readers to interactively explore the affective content of the text in more detail; e.g., in aggregated form as histograms, in sequential form following the order of text, and in detail embedded into the text display itself. Interaction techniques have been included to allow for filtering and navigating of text and visualizations.}, subject = {Mensch-Maschine-Kommunikation}, language = {en} } @incollection{vonLupinGeuderLeidingeretal., author = {von Lupin, Martin and Geuder, Philipp and Leidinger, Marie-Claire and Schr{\"o}der, Tobias and D{\"o}rk, Marian}, title = {Emosaic - Visualisierung von Emotionen in Texten durch Farbumwandlung zur Analyse und Exploration}, series = {DHd 2016 : Modellierung - Vernetzung - Visualisierung : die Digital Humanities als f{\"a}cher{\"u}bergreifendes Forschungsparadigma : Konferenzabstracts : Universit{\"a}t Leipzig, 7. bis 12. M{\"a}rz 2016}, booktitle = {DHd 2016 : Modellierung - Vernetzung - Visualisierung : die Digital Humanities als f{\"a}cher{\"u}bergreifendes Forschungsparadigma : Konferenzabstracts : Universit{\"a}t Leipzig, 7. bis 12. M{\"a}rz 2016}, publisher = {nisaba verlag}, address = {Duisburg}, isbn = {978-3-941379-05-3}, pages = {263 -- 266}, abstract = {Das computergest{\"u}tzte Extrahieren und Visualisieren von Emotionen in Texten ist eine etablierte Technik des "Distant Reading". Die generelle Stimmung eines Textes kann schnell erfasst werden ohne den gesamten Text lesen zu m{\"u}ssen. Da Emotionen sehr komplex und die Eigenschaften zwischen verschiedenen Emotionen fließend sind, ist die visuelle Charakterisierung von Emotionen schwierig. Wir stellen Emosaic vor, ein Online-Tool welches Emotionen aus benutzerdefinierten Texten filtert und durch systematische und nachvollziehbare Farbumwandlung zur Exploration und Analyse innerhalb einer interaktiven Visualisierung bereitstellt. Die durch drei Dimensionen beschreibbaren Emotionen werden dabei in klar definierte Farbparameter {\"u}bersetzt. Ein von uns entwickelter {\"o}ffentlich zug{\"a}nglicher Web- Prototyp ( vgl. Geuder et al. 2014-) zeigt anhand interaktiver Visualisierungen erste Analyse- und Explorationsm{\"o}glichkeiten dieser Methode.}, subject = {Geisteswissenschaften}, language = {de} }