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The current study examines a previously developed video game mood controlled vocabulary (CV) to empirically test its applicability and evaluate the individual terms\u2019 separability and distinctiveness.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The research team collected user reviews from Steam, an online game database. Three different games were selected for triangulation to represent each of the 17 moods identified in the existing CV, resulting in the selection of 51 games. Collected reviews were tokenized and investigated from individual, terminological and categorical levels of text analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Through the application of multiple analysis techniques (frequency, cluster and network), findings confirm the intuitiveness and usefulness of the existing CV. Additionally, opportunities for increased category separability and distinctness are identified for three moods: Aggressive, Quirky and Intense.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The current study adopts a user-centered perspective to evaluate the existing metadata framework created based on literature analysis. This study aims to complement the literature-based framework with users\u2019 perspectives to enhance the metadata for interactive multimedia resources, such as video games.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jd-10-2024-0247","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T16:30:46Z","timestamp":1739291446000},"page":"545-565","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating video game moods and their separability based on user-generated 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