Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Identifying Critical Factors for Successful Games by Applying Topic Modeling

  • Kwak, Mookyung (Dept. of e-Learning, Korea National Open University) ;
  • Park, Ji Su (Dept. of Computer Science and Engineering, Jeonju University) ;
  • Shon, Jin Gon (Dept. of e-Learning, Korea National Open University)
  • Received : 2020.09.15
  • Accepted : 2021.01.06
  • Published : 2022.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Games are widely used in many fields, but not all games are successful. Then what makes games successful? The question gave us the motivation of this paper, which is to identify critical factors for successful games with topic modeling technique. It is supposed that game reviews written by experts sit on abundant insights and topics of how games succeed. To excavate these insights and topics, latent Dirichlet allocation, a topic modeling analysis technique, was used. This statistical approach provided words that implicate topics behind them. Fifty topics were inferred based on these words, and these topics were categorized by stimulation-response-desiregoal (SRDG) model, which makes a streamlined flow of how players engage in video games. This approach can provide game designers with critical factors for successful games. Furthermore, from this research result, we are going to develop a model for immersive game experiences to explain why some games are more addictive than others and how successful gamification works.

Keywords

References

  1. Newzoo, "Global games market report," 2020 [Online]. Available: https://newzoo.com/products/reports/global-games-market-report/.
  2. Y. Hu, J. Boyd-Graber, B. Satinoff, and A. Smith, "Interactive topic modeling," Machine Learning, vol. 95, no. 3, pp. 423-469, 2014. https://doi.org/10.1007/s10994-013-5413-0
  3. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet allocation," The Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
  4. S. W. Kim and J. M. Gil, "Research paper classification systems based on TF-IDF and LDA schemes," Human-centric Computing and Information Sciences, vol. 9, article no. 30, 2019. https://doi.org/10.1186/s13673-019-0192-7
  5. D. G. Lee and Y. S. Seo, "Improving bug report triage performance using artificial intelligence based document generation model," Human-centric Computing and Information Sciences, vol. 10, article no. 26, 2020. https://doi.org/10.1186/s13673-020-00229-7
  6. F. Zhang, T. Y. Wu, J. S. Pan, G. Ding, and Z. Li, "Human motion recognition based on SVM in VR art media interaction environment," Human-centric Computing and Information Sciences, vol. 9, article no. 40, 2019. https://doi.org/10.1186/s13673-019-0203-8
  7. X. Tan, "Topic extraction and classification method based on comment sets," Journal of Information Processing Systems, vol. 16, no. 2, pp. 329-342, 2020. https://doi.org/10.3745/JIPS.04.0165
  8. Y. Yang, L. Li, Z. Liu, and G. Liu, "Abnormal behavior recognition based on spatio-temporal context," Journal of Information Processing Systems, vol. 16, no. 3, pp. 612-628, 2020. https://doi.org/10.3745/JIPS.02.0134
  9. Metacritic game [Online]. Available: https://metacritic.com/game.
  10. A. Amory, "Game object model version II: a theoretical framework for educational game development," Educational Technology Research and Development, vol. 55, no. 1, pp. 51-77, 2007. https://doi.org/10.1007/s11423-006-9001-x
  11. D. King, P. Delfabbro, and M. Griffiths, "Video game structural characteristics: a new psychological taxonomy," International Journal of Mental Health and Addiction, vol. 8, no. 1, pp. 90-106, 2010. https://doi.org/10.1007/s11469-009-9206-4
  12. N. S. Said, "An engaging multimedia design model," in Proceedings of the 2004 Conference on Interaction Design and Children: Building a Community, Baltimore, MD, 2004, pp. 169-172.
  13. M. W. Berry, S. T. Dumais, and G. W. O'Brien, "Using linear algebra for intelligent information retrieval," SIAM Review, vol. 37, no. 4, pp. 573-595, 1995. https://doi.org/10.1137/1037127
  14. T. Hofmann, "Unsupervised learning by probabilistic latent semantic analysis," Machine Learning, vol. 42, no. 1, pp. 177-196, 2001. https://doi.org/10.1023/a:1007617005950
  15. C. Sievert and K. Shirley, "LDAvis: a method for visualizing and interpreting topics," in Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Baltimore, MD, 2014, pp. 63-70.
  16. M. Corbetta and G. L. Shulman, "Control of goal-directed and stimulus-driven attention in the brain," Nature Reviews Neuroscience, vol. 3, no. 3, pp. 201-215, 2002. https://doi.org/10.1038/nrn755
  17. L. T. Lee and J. C. Hung, "Effects of blended e-Learning: a case study in higher education tax learning setting," Human-centric Computing and Information Sciences, vol. 5, article no. 13, 2015. https://doi.org/10.1186/s13673-015-0024-3
  18. Y. M. Choi, M. W. Choo, and S. A. Chin, "Prototyping a student model for educational games," Journal of Information Processing Systems, vol. 1, no. 1, pp. 107-111, 2005. https://doi.org/10.3745/jips.2005.1.1.107
  19. M. Goyal, D. Yadav, and A. Tripathi, "An in tuition istic fuzzy approach to classify the user based on an assessment of the learner's knowledge level in e-learning decision-making," Journal of Information Processing Systems, vol. 13, no. 1, pp. 57-67, 2017. https://doi.org/10.3745/JIPS.04.0011
  20. J. Lecinski, ZMOT: Winning the Zero Moment of Truth. Mountain, CA: Google, 2011.