Patches and Player Community Perceptions: Analysis of No Man’s Sky Steam Reviews

Authors

  • Chien Lu
  • Xiaozhou Li
  • Timo Nummenmaa
  • Zheying Zhang
  • Jaakko Peltonen

Keywords:

no man’s sky, player modeling, topic modeling

Abstract

Current game publishing typically involves an ongoing commitment to maintain and update games after initial release, and as a result the reception of games among players has the potential to evolve; it is then crucial to understand how players’ concerns and perception of the game are affected by ongoing updates and by passage of time in general. We carry out a data-driven analysis of a prominent game release, No Man’s Sky, using topic modeling based text mining of Steam reviews. Importantly, our approach treats player perception not as a single sentiment but identifies multiple topics of interest that evolve differently over time, and allows us to contrast patching of the game to evolution of the topics.

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Published

2020-01-01

Bibtex

@Conference{digra1303, title ="Patches and Player Community Perceptions: Analysis of No Man’s Sky Steam Reviews", year = "2020", author = "Lu, Chien and Li, Xiaozhou and Nummenmaa, Timo and Zhang, Zheying and Peltonen, Jaakko", publisher = "DiGRA", address = "Tampere", howpublished = "\url{https://dl.digra.org/index.php/dl/article/view/1303}", booktitle = "Proceedings of DiGRA 2020 Conference: Play Everywhere"}