Evaluating video game moods and their separability based on user-generated reviews

PurposeThe aboutness (a subject matter of resource) of information has been strongly emphasized when organizing and searching for different types of media resources. For video games, mood is one of the critical subjective elements that supports users in finding games of interest. The current study examines a previously developed video game mood controlled vocabulary (CV) to empirically test its applicability and evaluate the individual terms' separability and distinctiveness.Design/methodology/approachThe 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.FindingsThrough 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.Originality/valueThe 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' perspectives to enhance the metadata for interactive multimedia resources, such as video games.