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From amused to : enriching mood metadata by mapping textual descriptors to emojis for fiction reading

Wan-Chen Lee (School of Information Studies, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA)
Li-Min Cassandra Huang (Department of Library and Information Science, National Taiwan University, Taipei, Taiwan)
Juliana Hirt (School of Information Studies, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA)

Journal of Documentation

ISSN: 0022-0418

Article publication date: 9 January 2024

Issue publication date: 22 February 2024

214

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 : enriching mood metadata by mapping textual descriptors to emojis for fiction reading", Journal of Documentation, Vol. 80 No. 2, pp. 552-571. https://doi.org/10.1108/JD-08-2023-0146

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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