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2020
Conference Paper
Titel

A Community Detection Based Approach for Exploring Patterns in Player Reviews

Abstract
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.
Author(s)
Pielka, Maren
Sifa, Rafet
Ramamurthy, Rajkumar
Ojeda, César
Bauckhage, Christian
Hauptwerk
Intelligent Systems and Applications
Konferenz
Intelligent Systems Conference (IntelliSys) 2019
Thumbnail Image
DOI
10.1007/978-3-030-29516-5_43
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
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