User-centered categorization of mood in fiction

Abstract

PurposeReaders 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/approachThe 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.FindingsThe 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/valueThe 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.

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

“…The implications of the mapping between emojis and mood categories are twofold. For theoretical implications, we validated the three families of fiction mood categories in Cho et al (2023). Through analyzing the categories participants assigned, we verified the distinction among the three families which is a critical step before adopting it for practical application.…”

“…(1) to investigate the implementation of fiction mood descriptors by employing the theoretical notion of Cho et al's (2023) three families of mood categories, and (2) to establish mappings between emojis and the 30 fiction mood categories identified in Cho et al (2023).…”

“…In the second section, three multiple-choice questions were presented, and each question listed the 30 mood categories identified by Cho et al (2023). Participants were asked to select mood categories that best describe three aspects of a fiction book, respectively: (1) the Atmosphere/Setting, (2) their Emotion when/after they read and (3) the Tone/Narrative.…”

“…Notably, Spiteri and Jen (2018) introduced an affect taxonomy that encompasses nine emotions and 11 tone categories. Cho et al (2023) identified 30 mood categories and organized them into three families. These research endeavors laid the foundation for mood descriptions; yet, the formats are predominantly text-based.…”

“…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 mapping emojis to the existing textual metadata for fiction.…”