Enhancing video game metadata: empirical insights from gamer-reported data

Purpose This study aims to enrich video game mood metadata by incorporating gamer-provided data into the development of existing controlled vocabularies for video game mood. Adopting a user-centered approach, the study integrates gamers’ language and affective experiences to inform taxonomy design. Design/methodology/approach The research was conducted in two phases. First, an online survey of adult video game players collected participant-generated mood terms and their mappings to predefined mood categories. Terms that could not be mapped were labeled as No match. Second, these No match terms were analyzed through a collaborative card-sorting process to identify new categories, refine existing ones and exclude nonmood terms. Distributional and network analyses were conducted to examine relationships, consensus and separability among mood categories. Findings The findings support concrete revisions to existing controlled vocabularies, including the removal and reconceptualization of the Intense category, refinement of category definitions and the addition of new mood categories. The analyses also identify cases where category labels do not align with commonly used participant terms, and reveal varying degrees of overlap and separability among categories. Originality/value This study provides a user-centered framework for revising controlled vocabularies for video game mood by integrating gamer language and empirically derived category relationships into taxonomy design. By advancing an understudied area of mood metadata, it supports improved video game retrieval and recommendation, including mood-based search, filtering and similarity-based discovery.