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.
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References
Sifa, R., Drachen, A., Bauckhage, C.: Social Interaction in Virtual Worlds (2018)
Sifa, R.: Matrix and Tensor Factorization for Profiling Player Behavior. LeanPub, Victoria (2019)
Bauckhage, C., Cvejoski, K., Ojeda, C., Sifa, R.: Beyond spectral clustering: a comparative study of community detection for document clustering. In: Data Science - Analytics and Applications, 33–38 (2017)
Drescher, C., Wallner, G., Kriglstein, S., Sifa, R., Drachen, A., Pohl, M.: What moves players?: visual data exploration of Twitter and Gameplay data. In: Proceedings of CHI 2018, ACM (2018)
Sifa, R., Pawlakos, E., Zhai, K., Haran, S., Jha, R., Klabjan, D., Drachen, A.: Controlling the crucible: a novel PvP recommender systems framework for destiny. In: Proceedings of ACM ACSW IE (2018)
Meidl, M., Lytinen, S., Raison, K.: Using game reviews to recommend games. In: Proceedings of AAAI AIIDE (2014)
Ryan, J.O., Kaltman, E., Hong, T., Mateas, M., Wardrip-Fruin, N.: People tend to like related games. In: Proceedings of FDG (2015)
Ryan, J.O., Kaltman, E., Mateas, M., Wardrip-Fruin, N.: What We Talk About When We Talk About Games: Bottom-up Game Studies Using Natural Language Processing (2015)
Schütze, H., Manning, C.D., Raghavan, P.: Introduction to Information Retrieval, vol. 39. Cambridge University Press, Cambridge (2008)
von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Shuyo, N.: Language detection library for java (2010)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Utt, J., Springorum, S., Köper, M., Schulte im Walde, S.: Fuzzy V-Measure - An Evaluation Metric for Cluster Analysis of Ambiguous Data (2014)
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Pielka, M., Sifa, R., Ramamurthy, R., Ojeda, C., Bauckhage, C. (2020). A Community Detection Based Approach for Exploring Patterns in Player Reviews. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_43
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DOI: https://doi.org/10.1007/978-3-030-29516-5_43
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