From amused to : enriching mood metadata by mapping textual descriptors to emojis for fiction reading
ISSN: 0022-0418
Article publication date: 9 January 2024
Issue publication date: 22 February 2024
Abstract
Purpose
This study aims to explore the application of emojis to mood descriptions of fiction. The three goals are investigating whether Cho et al.'s model (2023) is a sound conceptual framework for implementing emojis and mood categories in information systems, mapping 30 mood categories to 115 face emojis and exploring and visualizing the relationships between mood categories based on emojis mapping.
Design/methodology/approach
An online survey was distributed to a US public university to recruit adult fiction readers. In total, 64 participants completed the survey.
Findings
The results show that the participants distinguished between the three families of fiction mood categories. The three families model is a promising option to improve mood descriptions for fiction. Through mapping emojis to 30 mood categories, the authors identified the most popular emojis for each category, analyzed the relationships between mood categories and examined participants' consensus on mapping.
Originality/value
This study focuses on applying emojis to fiction reading. Emojis were mapped to mood categories by fiction readers. Emoji mapping contributes to the understanding of the relationships between mood categories. Emojis, as graphic mood descriptors, have the potential to complement textual descriptors and enrich mood metadata for fiction.
Keywords
Acknowledgements
The authors disclosed receipt of the following financial support for the research and authorship of this article. This work was supported by the University of Wisconsin-Milwaukee Research Assistance Fund [grant number: 1014519800AAK7856].
Citation
Lee, W.-C., Huang, L.-M.C. and Hirt, J. (2024), "From amused to
Publisher
:Emerald Publishing Limited
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