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http://dx.doi.org/10.3745/JIPS.04.0236

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)
Publication Information
Journal of Information Processing Systems / v.18, no.1, 2022 , pp. 130-145 More about this Journal
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
Critical Factors; SRDG Model; Successful Game; Topic Modeling;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Y. Hu, J. Boyd-Graber, B. Satinoff, and A. Smith, "Interactive topic modeling," Machine Learning, vol. 95, no. 3, pp. 423-469, 2014.   DOI
2 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   DOI
3 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.   DOI
4 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.   DOI
5 J. Lecinski, ZMOT: Winning the Zero Moment of Truth. Mountain, CA: Google, 2011.
6 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   DOI
7 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.   DOI
8 Newzoo, "Global games market report," 2020 [Online]. Available: https://newzoo.com/products/reports/global-games-market-report/.
9 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.
10 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   DOI
11 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   DOI
12 X. Tan, "Topic extraction and classification method based on comment sets," Journal of Information Processing Systems, vol. 16, no. 2, pp. 329-342, 2020.   DOI
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.   DOI
14 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.   DOI
15 Metacritic game [Online]. Available: https://metacritic.com/game.
16 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.
17 T. Hofmann, "Unsupervised learning by probabilistic latent semantic analysis," Machine Learning, vol. 42, no. 1, pp. 177-196, 2001.   DOI
18 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.
19 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.   DOI
20 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.   DOI