Abstract
One way of subjective evaluation of games is through game reviews. These are critical analyses, aiming to give information about the quality of the games. While the experience of playing a game is inherently personal and different for each player, current approaches to the evaluation of this experience do not take into account the individual characteristics of each player. We firmly believe game review scores should take into account the personality of the player. To verify this, we created a game review score system, using multiple machine learning algorithms, that computes multiple review scores for different personalities which allow us to provide a more holistic perspective of this value, based on multiple and distinct player profiles. Our results support that the approach is statistically and significantly better than using the weighted average score provided by metacritic.com, currently one of the most popular websites that aggregate video game reviews, among other media products.
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References
Adams, E.: Fundamentals of Game Design, 3rd edn. Pearson Education, London (2014)
Bateman, C., Lowenhaupt, R., Nacke, L.: Player typology in theory and practice. In: DiGRA Conference (2011)
Bartle, R.: Hearts, clubs, diamonds, spades: players who suit MUDs. J. MUD Res. 1, 19 (1996)
Bouckaert, R.R., et al.: WEKA - experiences with a Java open-source project. J. Mach. Learn. Res. 11, 2533–2541 (2010)
Huang, J.: What can we recommend to game players?-Implementing a system of analyzing game reviews. University of Tampere, Tampere (2018)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11, 10–18 (2009)
Lazzaro, N.: Why we play games: four keys to more emotion without story. In: Game Developers Conference (2004)
Martinho, C., Santos, P., Prada, R.: Design e desenvolvimento de jogos. FCA (2014)
McCrae, R.R., John, O.P.: An introduction to the five-factor model and its applications. J. Pers. 60, 175–215 (1992)
McNamara, A.: Up against the wall: game makers take on the press. In: Game Developer’s Conference (2008)
Nacke, L.E., Bateman, C., Mandryk, R.L.: BrainHex a neurobiological gamer typology survey. Entertain. Comput. 5, 55–62 (2014)
Quandt, T., Kröger, S.: Multiplayer: The Social Aspects of Digital Gaming. Routledge, Abingdon (2014)
Rabin, S.: Introduction to Game Development, 2nd edn. Nelson Education, Toronto (2010)
Ribeiro, M.: Personalized game reviews. M.Sc. thesis. Instituto Superior Técnico, supervised by C. Martinho (2019)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, Burlington (2016)
Yee, N.: The gamer motivation profile what we learned from 250,000 gamers. In: Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, p. 2. ACM (2016)
VilaGames: FIFA 18 - PS4 n.d. https://www.vilagamesonline.com.br/produto/ps4/fifa-18-ps4-2
Acknowledgments
This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2019. We would also like to thank Prof. Layla Hirsh Martínez from Pontificia Universidad Católica del Perú for her advice and support in this work.
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Ribeiro, M., Martinho, C. (2019). Personalized Game Reviews. In: Zagalo, N., Veloso, A., Costa, L., Mealha, Ó. (eds) Videogame Sciences and Arts. VJ 2019. Communications in Computer and Information Science, vol 1164. Springer, Cham. https://doi.org/10.1007/978-3-030-37983-4_17
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