Browse > Article
http://dx.doi.org/10.7583/JKGS.2017.17.3.33

A Study of Factors Influencing Helpfulness of Game Reviews: Analyzing STEAM Game Review Data  

Kang, Ha-Na (Graduate School of Interaction Design, Hallym Univ.)
Yong, Hye-Ryeon (Graduate School of Interaction Design, Hallym Univ.)
Hwang, Hyun-Seok (Dept. of Business Administration, Hallym Univ.)
Publication Information
Journal of Korea Game Society / v.17, no.3, 2017 , pp. 33-44 More about this Journal
Abstract
With the development of the Internet environment, various types of online reviews are being generated and exchanged among consumers to share their opinions. In line with this trend, companies are making efforts to analyze online reviews and use the results in various business activities such as marketing, sales, and product development. However, research on online review in industry related to 'Video Game' which is representative experience goods has not been performed enough. Therefore, this study analyzed STEAM community review data using machine learning techniques. We analyzed the factors affecting the opinion of other users' game review. We also propose managerial implications to incease user loyalty and usability.
Keywords
Game Review; STEAM; Helpfulness; Big Data; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
연도 인용수 순위
1 Lee, H. G., Kwak H., (2013) Investigation of Factors Affecting the Effects of Online Consumer Reviews, Journal of Informatization Policy, 20(3). pp. 3-17.
2 2010 Survey on Internet Use, Korea Internet & Security Agency.
3 Yong, H. R., Kim, D. J., Hwang, H. S., (2015) A Study of Analyzing Realtime Strategy Game Data using Data Mining, Journal of Korea Game Society, 15(4), pp. 56-68.
4 Kim, J. K., (2016) Domestic and foreign big data trends and success cases, IE Magazine, 23(1), pp. 47-52.
5 Peterson, R.A & Maria, C. M (2003), Consumer Behavior(7th ed), Upper Saddle River, NJ: Prentice-Hall, Inc
6 Thopson, N. (2003), "More companies pay heed to their 'word of mouse' reputation", New York Times, June 23.
7 Bickart, B. & Schindler, R. (2001), Internet Forums as Influential Sources of Consumer Information, Journal of Interactive Marketing, 15(3), pp. 31-40.   DOI
8 Kim, J., Byeon, H., Lee, S. H., (2011) Enhancement of User Understanding and Service Value Using Online Reviews, The Journal of information systems, 20(2). pp. 21-36.   DOI
9 Chen, Y. & Xie, J. (2008), Online consumer review : Word of mouth as a new element or marketing communication mix, Management Science, 54(3) pp. 477-491.   DOI
10 Kang, H. N., Yong, H. R., & Hwang, H. S. (2016). Brand Clustering Based on Social Big Data: A Case Study. International Journal of Software Engineering and Its Applications, 10(4), pp. 27-36.
11 Hwang, H. S., (2016) Clustering Corporate Brands based on Opinion Mining: A Case Study of the Automobile Industry, Journal of the Korea Academia-Industrial cooperation Society, 17(11), pp. 453-462.   DOI
12 Cheon, Y., Kwak, K. T., (2015) Collective Sentiments and Users' Feedback to Game Contents : Analysis of Mobile Game UX based on Social Big Data Mining, Journal of Korea Game Society, 15(4), pp. 145-156.   DOI
13 Jang, P. S., (2014) Study on Principal Sentiment Analysis of Social Data, Journal of the Korea Society of Computer and Information, 19(12), pp. 49-56.   DOI
14 Kim, M. J., Song, E. J., Kim, Y. H., (2016) A Design of Satisfaction Analysis System For Content Using Opinion Mining of Online Review Data , Journal of Internet Computing and Services, 17(3), 107-113.   DOI
15 Kim, K., Kim, D. Y., Lee, J. H., (2014) Measuring Similarity Between Movies Based on Polarity of Tweets, Journal of Korean Institute of Intelligent Systems, 24(3), pp. 292-297.   DOI
16 Song, E. J., (2015) The Sensitivity Analysis for Customer Feedback on Social Media, Journal of the Korea Institute of Information and Communication Engineering, 19(4), pp. 780-786.   DOI
17 B. Liu, (2012), Sentiment Analysis and opinion mining(Synthesis Lectures on Human Language Technologies), Morgan & ClayPool
18 Mudambi, S. M. & Schuff, D. (2010). What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com, MIS Quarterly, 34(1), pp. 185-200.   DOI
19 Kwon, J., Lee, M., (2012) A Study on the Determining Factors of Online Review Helpfulness, Proceedings of Korea Intelligent Information Systems Society. pp. 205-211.
20 Josang, A. & smail, R. & Boyd, C. (2007), A survey of trust and reputation systems form online service provision, Decision support systems, 43(2), pp. 618-644.   DOI
21 Cheung, M. Y. & Luo, C. & Sia, C.L. & Chen, H. (2009), Credibility of Electronic Word of- Mouth: Informational and Normative Determinants of On-line Consumer Recommendations, International Journal of Electronic Commerce, 13(4), pp. 9-38.   DOI
22 Briggs, P. & Burford, B. & De Angeli, A & Lynch, P. (2002), Trust in online advice, Social Science Computer Review, 20(3), pp. 321-332.   DOI
23 Forman, C. & Ghose, A. & Wiesenfeld, B. 2008, Do online reviews matters? - An empirical investigation of panel data, Decision Support Systems, 45(4), pp. 1007-1016.   DOI
24 Jeon, C. H., (2012), Data Mining Techniques, Hannarea.
25 Jensen, J. R., Qiu, F., M. Ji,(1999), Predictive Modeling of coniferous Forest Age Using Statistical and Artificial Neural network Approaches Applied to Remote Sensing Data, International Journal of Remote Sensing, 20(14), pp. 2805-2822.   DOI
26 Rao, V. B., H. V. Rao, (2993), "C++ Neural Network and Fuzzy Logic," New York: Management Information, pp. 408.
27 Hengl, T. (1993), Neural Network : A Neural computing Primer, Personal Computing Artificial Intelligence, 16(3), pp. 32-43.
28 Cortes, C., Vapnik, V., (1995), Support-vector network, Machine Learning 20, pp.273-297
29 Kim, E., Jhun, M., Bang, S., (2016) Hierarchically penalized support vector machine for the classification of imbalanced data with grouped variables, The Korean journal of applied statistics, 29(5), pp. 961-975.   DOI
30 Park, J., Chae, M., Jung, S., (2016) Classification Model of Types of Crime based on Random-Forest Algorithms and Monitoring Interface Design Factors for Real-time Crime Prediction, 22(9), pp. 455-460.   DOI
31 Jeong, S. K., Choi, M. S., Kim, H. S., (2016) Coreference Resolution for Korean Using Random Forests, KIPS transactions on software and data engineering,, 5(11), pp. 535-540.   DOI
32 Lee, S. H., Lee, L. S., & Hwang, H. S. (2017). Does Social Opinion Influence Movie Ticket Revenues?: A Case Study. Advanced Science Letters, 23(3), 1627-1630.   DOI
33 Kim, I. J., (2014) Big Data Deep Learning: A New Trend in Machine Learning, The Journal of Korean Institute of Communications and Information Sciences, 31(11), pp. 52-57.
34 Choi, Y. S., (2015), Current status and future of Deep Learning and Intelligent Technology, IE Magazine, 22(2), pp. 31-35.