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A Community Detection Based Approach for Exploring Patterns in Player Reviews

: Pielka, Maren; Sifa, Rafet; Ramamurthy, Rajkumar; Ojeda, César; Bauckhage, Christian


Bi, Y.:
Intelligent Systems and Applications : Proceedings of the 2019 Intelligent Systems Conference (IntelliSys), Volume 1; September 5-6, 2019, in London, UK
Cham: Springer International Publishing, 2020 (Advances in Intelligent Systems and Computing 1037)
ISBN: 978-3-030-29515-8 (Print)
ISBN: 978-3-030-29516-5 (Online)
Intelligent Systems Conference (IntelliSys) <2019, London>
Conference Paper
Fraunhofer IAIS ()

Optimizing player retention and engagement by providing tailored game content to their audience remain as a challenging task for game developers. Tracking and analyzing player engagement data such as in-game behavioral data as well as out-game, such as online text reviews or social media postings, are crucial in identifying user concerns and capturing user preferences. In particular, studying and understanding user reviews has therefore become an integral component of any game development process and is pursued as a research area actively. In this paper, we are interested in extracting latent and influential topics by analyzing text reviews on a popular game community website. Towards addressing this, we present an exploratory analysis with the application of a hierarchical community detection-based hybrid algorithm that extract topics from a given corpus of game reviews. Our analysis reveals interesting topics and sub-topics which can be used for further downstream analysis.