Regression Modeling of Reader’s Emotions Induced by Font Based Text Signals
In this work we presented a mathematical model for the readers’ emotional state responses triggered by font style, type and color. It is based on multiple regression analysis of the repeated measures from 45 students and for 35 textual stimuli using the Self-Assessment Manikin test. Based on the dimensional theory of emotions, we propose a model on how emotional dimensions Pleasure, Arousal, and Dominance vary according to the typographic text signals: font style, font type and font/background color combinations. We observe that “Pleasure” dimension is affected negatively by font type (“Arial” and “Times New Roman”) and positively by color brightness difference of font/background color combinations. “Arousal” and “Dominance” are affected only by color brightness difference (negative correlation). According to the proposed model, font type “Arial” elicits more pleasant emotional state than “Times New Roman”. The results can be applied to augment user interface experience or to add expressivity in Text-to-Speech systems and provide accessibility of typography induced text signals.
Keywordsdocument accessibility text signals reader’s emotions Text-to-Speech Self-Assessment Manikin test
Unable to display preview. Download preview PDF.
- 1.Kouroupetroglou, G., Tsonos, D.: Multimodal Accessibility of Documents. In: Pinder, S. (ed.) Advances in Human-Computer Interaction, pp. 451–470. I-Tech Education and Publishing, Vienna (2008)Google Scholar
- 2.Fellbaum, K., Kouroupetroglou, G.: Principles of Electronic Speech Processing with Applications for People with Disabilities. Technology and Disability 20(2), 55–85 (2008)Google Scholar
- 3.McLuhan, M., Fiore, Q.: The Medium is the Message. Gingko Press, Berkeley (2005)Google Scholar
- 9.Lang, P.J., Bradley, M., Culthbert, B.: International affective picture system (IAPS): instruction manual and affective ratings. Technical Report A-6, The Center for Research in Psychophysiology, University of Florida, USA (2005)Google Scholar
- 11.Hinkle, D.E., Wiersma, W., Jurs, S.G.: Applied Statistics for the Behavioral Sciences, 5th edn. Wadsworth Publishing (2002)Google Scholar
- 12.Holsclaw, T.N.: Investigation of repeated measures linear regression methodologies. Master Thesis, Faculty of the Department of Mathematics, San Jose State University (2007)Google Scholar
- 14.OriginLab, http://www.originlab.com/
- 18.Tsonos, D., Kouroupetroglou, G.: Modeling Reader’s Emotional State Response on Document’s Typographic Elements. In: Advances in Human-Computer Interaction, 18 pages (2011), doi:10.1155/2011/206983Google Scholar
- 20.Emotion Markup Language (EmotionML) 1.0, http://www.w3.org/TR/emotionml/
- 22.Freitas, D., Kouroupetroglou, G.: Speech Technologies for Blind and Low Vision Persons. Technology and Disability 20(2), 135–156 (2008)Google Scholar