This study aims to understand the behaviors, motivations, and gameplay types of videogame players through topic modelling, sentiment clusters, and valence analysis of videogame reviews.

A branch of academic videogame studies called player type resarch seeks to understand why and how different players engage with videogames. This research gathers user reviews from Steam platform which are run through semantic cluster and valence analysis. This provides the results of what videogame components lead the players’ perceptions of videogames, and how the players approach to each cluster sentimentally. To understand the player types, proximity analysis is also performed on valence/cluster data.