User-centered categorization of mood in fiction
| DOI | https://doi.org/10.1108/JD-03-2022-0071 |
| Published date | 23 August 2022 |
| Date | 23 August 2022 |
| Pages | 567-588 |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
| Author | Hyerim Cho,Wan-Chen Lee,Li-Min Huang,Joseph Kohlburn |
User-centered categorization of
mood in fiction
Hyerim Cho
School of Information Science and Learning Technologies, University of Missouri,
Columbia, Missouri, USA
Wan-Chen Lee
School of Information Studies, University of Wisconsin-Milwaukee,
Milwaukee, Wisconsin, USA
Li-Min Huang
School of Information Sciences, The University of Tennessee Knoxville,
Knoxville, Tennessee, USA, and
Joseph Kohlburn
School of Information Science and Learning Technologies, University of Missouri,
Columbia, Missouri, USA
Abstract
Purpose –Readers articulate mood in deeply subjective ways, yet the underlying structure of users’
understanding of the media they consume has important implications for retrieval and access. User
articulations might at first seem too idiosyncratic, but organizing them meaningfully has considerable
potential to provide a better searching experience for all involved. The current study develops mood categories
inductively for fiction organization and retrieval in information systems.
Design/methodology/approach –The authors developed and distributed an open-ended survey to 76
fiction readers to understand their preferences with regard to the affective elements in fiction. From the fiction
reader responses, the research team identified 161 mood terms and used them for further categorization.
Findings –The inductive approach resulted in 30 categories, including angry, cozy, dark and nostalgic.
Results include three overlapping mood families: Emotion, Tone/Narrative, and Atmosphere/Setting, which in
turn relate to structures that connect reader-generated data with conceptual frameworks in previous studies.
Originality/value –The inherent complexity of “mood”should not dissuade researchers from carefully
investigating users’preferences in this regard. Adding to the existing efforts of classifying moods conducted
by experts, the current study presents mood terms provided by actual end-users when describing different
moods in fiction. This study offers a useful roadmap for creating taxonomies for retrieval and description, as
well as structures derived from user-provided terms that ultimately have the potential to improve user
experience.
Keywords Fiction, Categories, User warrant, Mood, Affective information needs, Pleasure reading, Card
sorting, Metadata
Paper type Research paper
Introduction
Mood is a deep and complex notion. It refers to various concepts across different domains,
such as affect in psychology (Bartsch and Oliver, 2011), emotions and tones in literature
(Hogan, 2011), and mood in music information retrieval (MIR) as well as other user studies in
information science (Cho et al., 2021a;Hu, 2010). Mood has been understood in various ways,
but even with the variances in specific meaning across different domains, existing literature
agrees on one thing: Mood is an important element for media information users, especially for
the ones who seek leisure materials (Hogan, 2011;Vorderer and Reinecke, 2015). Several
search and media recommendation systems have implemented mood as one of the primary
access points for their resources, including music streaming and recommending services,
Categorization
of mood in
fiction
567
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0022-0418.htm
Received 30 March 2022
Revised 27 July 2022
Accepted 31 July 2022
Journal of Documentation
Vol. 79 No. 3, 2023
pp. 567-588
© Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-03-2022-0071
such as Spotify [1] and Pandora [2],and films and TV show recommendation systems like
Netflix [3].
Researchers in literature and psychology domains have tried to collect and categorize
emotions,tones,oraffect (Clore et al., 1987;Thomson and Crocker, 2013;Laurier et al., 2010;
Ortony et al., 1988) to understand the diversity of human emotions. However, the authors of
this study find there is a need to organize and understand the moods, particularly for fiction
readers from the information science perspective. Fiction books in libraries and other relevant
information systems support browsing by subjects (such as the Library of Congress Subject
Headings (LCSH) search) and known-item searches (e.g. search by title or author). Often,
subjects listed for fiction do not describe the narrative efficiently or inclusively, and get mixed
up with “genre”terms, as seen in Figure 1. In other situations, when subject terms describe the
narrative of a fiction book too specifically, it might potentially spoil the fun part of pleasure
reading. Utilizing mood terms (e.g. depressing, light-hearted, dark, funny) can both address
this problem by focusing on enriching the description of the aboutness of a work of fiction
Figure 1.
A bibliographic record
of the fiction book,
Hard-Boiled
Wonderland and the
End of the World
JD
79,3
568
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