From amused to : enriching mood metadata by mapping textual descriptors to emojis for fiction reading
| Date | 09 January 2024 |
| Pages | 552-571 |
| DOI | https://doi.org/10.1108/JD-08-2023-0146 |
| Published date | 09 January 2024 |
| 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 | Wan-Chen Lee,Li-Min Cassandra Huang,Juliana Hirt |
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, and
Juliana Hirt
School of Information Studies, University of Wisconsin-Milwaukee,
Milwaukee, Wisconsin, USA
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 Emoji, Fiction, Categories, Mood, Emotion, Metadata
Paper type Article
Introduction
Designing user-friendly information systems that account for both emotion and diversity has
stepped to the forefront in society, especially with the increasing popularity of social media and
streamingservices.However, thequestion of howto incorporateemotion in informationsystems
to serve diverse users has yet to be fully explored. Some studies focused on automatically
identifying and sorting the emotions in information (e.g. tweets) th rough sentiment analysis
(Hauthal et al., 2019) and recognized the limitations of textual emotion descriptions. For example,
natural language is complex and context-dependent, making it challenging for computational
analysis. Also, many analyses and models are based on English data sets. This leads to variant
levels of precision when analyzing multi-lingual content (Chen et al., 2019).
JD
80,2
552
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].
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 2 August 2023
Revised 13 November 2023
Accepted 23 November 2023
Journal of Documentation
Vol. 80 No. 2, 2024
pp. 552-571
© Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-08-2023-0146
The limitations of textual emotion descriptions motivated scholars to explore emojis,
graphic expressions of emotions. While studies found that emoji interpretations are under
social and cultural influences (Freedman, 2018;Swiftkey, 2015), and emojis are not accessible
to all users (e.g. not all screen readers read emojis), scholars recognized their potential to
complement textual descriptions (Barbieri et al., 2016;Shamsi et al., 2020). By adding to
textual descriptions, emojis could serve users with limited levels of language skills, such as
children and language learners. As Aliannejadi et al. (2020) suggested, applying emojis to
information systems may improve children’s information retrieval experiences. To better
incorporate emojis to information systems, there have been attempts to map emojis to
emotion categories (e.g. joy and sadness) through analyzing users’emoji usages with machine
learning approaches (Guibon et al., 2023;Matsumoto et al., 2018;Okamoto and Ochiai, 2021).
Inspired by previous research, we aim to explore the application of emojis to the
description of fiction. The reason for focusing on fiction is a three-fold. First, fiction is a
popular type of pleasure reading, in which emotion/mood plays a significant role. Readers
may pursue a particular emotional experience (e.g. an escapist feeling) to cope with their
current emotional and physical distress through reading fiction (Ross, 1999). Since emojis
describe emotions, this study focuses on fiction to investigate emoji applications. The second
reason is rooted in the view of treating emojis as metadata. The library community has been
creating metadata for fiction to support search and recommendation services (American
Library Association, 2000;Beghtol, 1994;Cho et al., 2023;EBSCO, 2023;Saarti, 2019). In this
study, we explored the possibility of enriching mood/emotion metadata by mappingemojis to
the existing textual metadata for fiction. Lastly, the focus on fiction is motivated by the
opportunities to better serve readers. While research acknowledges the significance of mood
in fiction, most current information systems organize works of fiction by genre, popularity,
etc. Only a few systems apply textual mood metadata or emojis, such as Whichbook [1] and
Amazon Kindle Books. These applications highlight the potential for increasing mood/
emotion metadata applications in systems for fiction. Through mapping emojis to textual
emotion categories, we aim to encourage a wider application of emojis to fiction descriptions.
There are three goals in this study. First, investigating whether Cho et al.’s model (2023), as
presented in Figure 1, is a sound conceptual framework for implementing emojis and mood
categories in information systems. In this previous study, the authors collected 161 reader-
assigned mood terms describing works of fiction. Through the card sorting method, the 161
terms were categorized into 30 mood categories based on similarity of meanings, co-
occurrences and form variances. In each category, the most frequently assigned term (e.g.
Angry) was the category name that represented the other terms (e.g. Annoying, Mad and
Wrath). Based on the OCC emotional model (a model proposed by Ortony, Clore, and Collins in
1988) developed in previous studies (Clore et al., 1987;Clore and Ortony, 2013;Ortony et al.,
1988), Cho et al. suggested incorporating three non-mutually exclusive families of affective
concepts: Atmosphere/Setting, Emotion and Tone/Narrative into fiction mood descriptions to
better capture affective nuances. While a mood category may belong to more than one family
(e.g. Excited can describe the Atmosphere/Setting of a book or the Emotion of a reader), it is
argued that differentiating the three families of mood could increase the granularity and
precision of mood description. Considering Cho et al.’s model is a recent conceptual
framework with solid theoretical foundation and a specific focus on fiction; we aimed to verify
this argument by examining if readers differentiate the three families. The verification would
suggest whether the model is a sound choice for applying mood categories and emojis to
information systems. The second goal of this study is to aid the incorporation of emojis to
textual mood/emotion descriptions by mapping 115 face emojis (Unicode.org, 2022) to the 30
mood categories. The third goal is the exploration and visualization of the relationships
between mood categories based on the emojis mapping. We answered two research questions
in this study:
Mapping
textual
descriptors
to emojis
553
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