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Advances in Human-Computer Interaction
Volume 2011 (2011), Article ID 206983, 18 pages
Research Article

Modeling Reader's Emotional State Response on Document's Typographic Elements

Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis, 157 84 Athens, Greece

Received 30 July 2010; Revised 31 January 2011; Accepted 25 March 2011

Academic Editor: Kerstin S. Eklundh

Copyright © 2011 Dimitrios Tsonos and Georgios Kouroupetroglou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present the results of an experimental study towards modeling the reader's emotional state variations induced by the typographic elements in electronic documents. Based on the dimensional theory of emotions we investigate how typographic elements, like font style (bold, italics, bold-italics) and font (type, size, color and background color), affect the reader's emotional states, namely, Pleasure, Arousal, and Dominance (PAD). An experimental procedure was implemented conforming to International Affective Picture System guidelines and incorporating the Self-Assessment Manikin test. Thirty students participated in the experiment. The stimulus was a short paragraph of text for which any content, emotion, and/or domain dependent information was excluded. The Analysis of Variance revealed the dependency of (a) all the three emotional dimensions on font size and font/background color combinations and (b) the Pleasure dimension on font type and font style. We introduce a set of mapping rules showing how PAD vary on the discrete values of font style and font type elements. Moreover, we introduce a set of equations describing the PAD dimensions' dependency on font size. This novel model can contribute to the automated reader's emotional state extraction in order, for example, to enhance the acoustic rendition of the documents, utilizing text-to-speech synthesis.