DOI QR코드

DOI QR Code

A Study on the Effects of Online Word-of-Mouth on Game Consumers Based on Sentimental Analysis

감성분석 기반의 게임 소비자 온라인 구전효과 연구

  • 정근웅 (성균관대학교 일반대학원 경영학과) ;
  • 김종욱 (성균관대학교 경영전문대학원)
  • Received : 2017.12.22
  • Accepted : 2018.03.20
  • Published : 2018.03.28

Abstract

Unlike the past, when distributors distributed games through retail stores, they are now selling digital content, which is based on online distribution channels. This study analyzes the effects of eWOM (electronic Word of Mouth) on sales volume of game sold on Steam, an online digital content distribution channel. Recently, data mining techniques based on Big Data have been studied. In this study, emotion index of eWOM is derived by emotional analysis which is a text mining technique that can analyze the emotion of each review among factors of eWOM. Emotional analysis utilizes Naive Bayes and SVM classifier and calculates the emotion index through the SVM classifier with high accuracy. Regression analysis is performed on the dependent variable, sales variation, using the emotion index, the number of reviews of each game, the size of eWOM, and the user score of each game, which is a rating of eWOM. Regression analysis revealed that the size of the independent variable eWOM and the emotion index of the eWOM were influential on the dependent variable, sales variation. This study suggests the factors of eWOM that affect the sales volume when Korean game companies enter overseas markets based on steam.

배급사가 소매점을 통해 게임을 유통했던 과거와 다르게 현재는 디지털 콘텐츠인 게임을 온라인 기반의 유통채널을 활용하여 판매를 실시하고 있다. 본 연구는 온라인 디지털 콘텐츠 유통 채널인 스팀(Steam)에서 판매되는 게임의 판매량에 대해서 eWOM(전자구전효과)의 요인들이 어떤 영향을 미치는지 분석한다. 최근 빅데이터 기반의 데이터 마이닝 기법을 이용한 연구가 많이 진행되고 있는데, 본 연구에서 eWOM의 요인 중 각 리뷰의 감성을 분석할 수 있는 텍스트 마이닝 기법인 감성분석을 실시하여 eWOM의 감성지수를 도출한다. 감성분석은 나이브 베이즈(Naive Bayes)와 지지벡터기(SVM) 분류기를 활용하고, 정확도가 높은 지지벡터기(SVM) 분류기를 통해 감성지수를 산출한다. 도출한 감성지수와 eWOM의 크기인 각 게임의 리뷰의 수, eWOM의 평점인 각 게임의 유저점수를 독립변수로 하여 종속변수인 판매변화량에 대해서 회귀분석을 실시한다. 회귀분석 결과, 독립변수인 eWOM의 크기와 eWOM의 감성지수가 종속변수인 판매변화량에 영향을 미치는 것을 확인하였다. 본 연구는 연구결과를 통해 국내 게임 기업들이 스팀을 기반으로 해외진출 시 판매량에 영향을 미치는 eWOM의 요인들을 제시할 수 있는 시사점을 가진다.

Keywords

References

  1. H. J. Jang. (2017. 3. 31). Worldwide attention, Steam 1st battle ground, Game performance is the most important. THIS IS GAME. http://www.thisisgame.com/webzine/news/nboard/5/?n=70753
  2. S. Y. Park. (2015. 2. 27). Steam, active account exceeded 125 million, ZD NET Korea. http://www.zdnet.co.kr/news/news_view.asp?artice_id=20150227091302.
  3. Christiansen. T & S. S. Tax. (2000). Measuring Word of Mouth: The Questions of Who and When. Journal of Marketing Communications, 6(3), 185-199. https://doi.org/10.1080/13527260050118676
  4. Goh, J. M., G. G. Gao. & R. Agarwal. (2016). The creation of social value: Can an online health community reduce rural-urban health disparities?. Management Information Systems Quarterly, 40(1), 247-263. https://doi.org/10.25300/MISQ/2016/40.1.11
  5. S. R. Back. (2005). An exploratory study of motives toward word of mouth activities on the Internet. The Korean Journal of Advertising and Public Relations, 7(1), 108-144.
  6. Zhang. W. & S. Watts. (2003). Knowlege Adoption in Online Communities of Practice. International Conference on Information Systems, Atlanta, AIS, 35(3), 96-109.
  7. Henning-Thurau. T., K. P. Gwinner., G. Walsh. & D. D. Gremler. (2004). Electronic Word-of-Mouth via Consumer-Opinion Platforms: What Motives Consumers to Articulate Themselves on the Internet?. Journal of Interactive Marketing, 18(1), 32-52. https://doi.org/10.1002/dir.20004
  8. Chatterjee. P. (2001). Online Review - Do Consumers Use Them?. Advances in Consumer Research, 28, 129-133.
  9. Mcknight. H. D., V. Choudhury., & C. Kacmar. (2002). Developing and Validating Trust Measures for e-Commerce: An Integrative Typology. Information Systems Research, 13(3), 334-359. https://doi.org/10.1287/isre.13.3.334.81
  10. Brown. J. J., A. J. Broderick., & N. Lee. (2007). Word of Mouth Communication with Online Communication : Conceptualizing the Online Social Network. Journal of Interactive Marketing, 21(3), 2-20. https://doi.org/10.1002/dir.20082
  11. Schindler. R. M. & B. Bickart.(2005, January). Published Word of Mouth: Referable, Consumer-Generated Information on the Internet. In C. P. Haugtvedt, K. A. Machleit and R. F. Yalch(eds.) .(pp. 35-61). Online Consumer Psychology, NJ: Lawrence Erlbaum Associates.
  12. I. K. Kim. (2016). The dynamics of online word-of-mouth and marketing performance : exploring mobile game application reviews using text-mining and machine-learning. ph.D. dissertation. Korea University, Seoul.
  13. B. Y. Choi. (2017). Understanding and application of consumer behavior. Seoul : Parkyongsa.
  14. H. S. Byeon & M. S. Yim. (2014). The Impact of Users' Congruity and Emotion on Intention to Game Use. Journal of Digital Convergence, 12(11), 89-98. https://doi.org/10.14400/JDC.2014.12.11.89
  15. Y. J. Jo. (2015). A Study on the Influence of Connectivity and Convenience of Smartphones of Word-of mouth Intentions in the Convergence Era : Focused on the Mediating Effects of Application. Journal of Digital Convergence, 13(5), 69-78. https://doi.org/10.14400/JDC.2015.13.5.69
  16. D. S. Yorm. (2016). Factors Affecting User Satisfaction of Mobile Social Network Games : Focusing on the Quality and Self-determination. Journal of Digital Convergence, 14(11), 459-467. https://doi.org/10.14400/JDC.2016.14.11.459
  17. D. S. Youm. (2017). The Effect of Perceived Enjoyment and User Characteristics on Intention of Continuous Use of Mobile Social Network Games : Focusing on Mediating Effect of Flow Experience. Journal of Digital Convergence, 15(9), 415-425. https://doi.org/10.14400/JDC.2017.15.9.415
  18. J. W. Kang. (2008). Game and Culture Research. Seoul : Communication Books.
  19. S. T. Park., H. C. Lee., T. U. Kim & S. M. Choi. (2012). A Study on Factors Influencing Attachment of Gamers to MMORPG On-line Games. Journal of Digital Convergence, 10(2), 109-119. https://doi.org/10.14400/JDPM.2012.10.2.109
  20. Dang, Shilpa & Peerzada Hamid Ahmad. (2014). Text Mining: Techniques and its Application. International Journal of Engineering & Technology Innovations, ISSN (Online) : 2348-0866, 1(4), 22-25.
  21. C. N. Jun. & I. O. Seo. (2013). Analyzing the Bigdata for Practical Using into Technology Marketing : Focusing on the Potential Buyer Extraction. Korean Strategic Marketing Association, 21(2), 181-203.
  22. Dang, Dr. Shilpa & Peerzada Hamid Ahmad. (2015). A Review of Text Mining Techniques Associated with Various Application Areas. International Journal of Science and Research (IJSR), 4(2), 2461-2466. https://doi.org/10.21275/v4i11.NOV151645
  23. S. H. Seo & J. T. Kim. (2016). Deep Learning Based Emotion Analysis Research Trend. Korea Multimedia Society, 20(3), 8-22.
  24. Chen, H. & Zimbra. D. (2010). AI and opinion mining. Intelligent Systems. IEEE, 25(3), 74-80.
  25. Nasukawa. T. & Yi. J. (2003). Sentimentanalysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture. (pp. 70-77). ACM.
  26. O'Connor. B., Balasubramanyan. R., Routledge B. R., & Smith. N. A. (2010). From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. ICWSM, 11, 122-129.
  27. Hu. M. & Liu. B. (2004). Mining and summarizing customer reviews. KDD'04 Proceedings of the tenth international conference on knowledge discovery and data mining. (pp. 168-177). ACM SIGKDD.
  28. Esulim. A. & Sebastiani. F. (2006). SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. Proceedings of LREC. (pp. 417-422). ITALY
  29. Baccianella. S., Esuli. A. & Sebastiani. F. (2010). SentiWordNet 3.0: An Enganced Lexical Resource for Sentiment Analysis and Opinion Mining. LREC, 10, 2200-2204.
  30. Liu. S. M & Chen. J. H. (2015). A multi-label classification based approach for sentiment classification. Expert Systems with Applications, 42(3), 1083-1093. https://doi.org/10.1016/j.eswa.2014.08.036
  31. Arcjak. N., A. Ghose & P. G. Ipeirotis. (2007). Show me the money!. Proceedings on the 13th International Conference. (pp. 56-65). ACM SIGKDD.
  32. Archak. N., A. Ghose, & P. G. Ipeirotis. (2011). Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Management Science, 57(8), 1485-1509. https://doi.org/10.1287/mnsc.1110.1370
  33. Berger. J., A. T. Sorensen. & S. J. Rasmussen. (2010). Positive Effects of Negative Publicity: When Negative Reviews Increase Sales. Marketing Science, 29(5), 815-827. https://doi.org/10.1287/mksc.1090.0557
  34. W. J. Chu. & M. J. Roh. (2014). Exploring the Role of Preference Heterogeneity and Causal Attribution in Online Ratings Dynamics. Asia Marketing Journal, 15(4), 61-101.
  35. J. H. Lee, S. Hong & D. Kang. (2014). The Marketing Success Factors of Hyundai Card Company: Business Model, Development of Goods and BTL Marketing. Korea Business Review, 18(3), 147-170.
  36. Chintagunta. P. K., S. Gopinath & S. Venkataraman. (2010). The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets. Marketing Science, 29(5), 944-957. https://doi.org/10.1287/mksc.1100.0572
  37. Dellarocas. C., G. Gao. & R. Narayan. Are Consumers More Likely to Contribute Online Reviews for Hit or Niche Products?. Journal of Management Information Systems. 27(2), 127-157. https://doi.org/10.2753/MIS0742-1222270204
  38. H. K. Lee & H. Kwak. (2013). Investigation of Factors Affecting the Effects of Online Consumer Reviews. Informatization policy, 20(3), 3-17.
  39. H. W. Hwangbo & J. H. Kim. (2016). A Study on the Factors Affecting to the Export Performance for Korean Drama Using Sentimental Analysis. The e-Business Studies, 17(6), 87-99. https://doi.org/10.20462/tebs.2016.12.17.6.87
  40. Y. Liu. (2006). Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of Marketing, 70(3), 74-89. https://doi.org/10.1509/jmkg.70.3.74
  41. Y. K. Kim. (2013). A Study on Relationship between the Relationship Benefit, Customer Satisfaction and Loyalty of Internet Shopping Malls. Daehan Academy of Management Information Systems, 32(4), 155-187.
  42. Sawhney. M. S. & J. Eliashberg. (1996). A parsimonious model for forecasting gross box-office revenues of motion pictures. Marketing Science, 15(2), 113-131. https://doi.org/10.1287/mksc.15.2.113
  43. Duan. W., B. Gu. & B. Whinston. (2008). Do online reviews matter?: An empirical investigation of panel data. Decision Support Systems, 45(4), 1007-1016. https://doi.org/10.1016/j.dss.2008.04.001
  44. Pang. B., Lee. L. & Vaithyanathan. S. (2002). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing. (pp. 79-86). ACM.
  45. Manning. C. D., Raghavan. P. & Schutze. H. (2008). Introduction to Information Retrival. Cambrige: Cambridge university press, 1(1).
  46. Tay. F. E. & Cao. L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29(4), 309-317. https://doi.org/10.1016/S0305-0483(01)00026-3
  47. Tong. S. & Koller. D. (2002). Support Vector Machine Active Learning with Applications to Text Classification. The Journal of Machine Learning Research, 2, 45-66.
  48. Pak. A. & P. Paroubek. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of the Seventh International Conference on Language Resources and Evaluation(pp. 1320-1326). Valletta.
  49. Kumar. A. & Sebastian. T. M. (2012). Sentiment Analysis on Twitter Issue. IJCSI, 9(3), 372-378
  50. H. J. Kim, K. H. Han & S. S. Shin. (2017). Crepe Search System Design using Web Crawling, Journal of Digital Convergence, 15(11), 261-269. https://doi.org/10.14400/JDC.2017.15.11.261
  51. Y. Y. Kim & M. Song. (2016). A Study on Analyzing Sentiments on Movie Reviews by Multi-Level Sentiment Classifier, Journal of intelligence and information systems, 22(3), 71-89. https://doi.org/10.13088/jiis.2016.22.3.071
  52. P. G. Preethi., V. Uma & Ajit kumar. (2015). Temporal Sentiment Analysis and Causal Rules Extraction from Tweets for Event Prediction. Procedia Computer Science, 48, 84-89. https://doi.org/10.1016/j.procs.2015.04.154
  53. Coovert. M. D. & G. D. Reeder. (1990). Negativity Effects in Impression Formation: The Role of Unit Formation and Schematic Expectations. Journal of Experimental Social Psychology, 26(1), 49-62. https://doi.org/10.1016/0022-1031(90)90061-P
  54. J. S. Kim, T. Y. Lee, T. G. Kim & H. W. Jung. (2015). Studies on the development scheme and the current state of Korea Game Industry. Journal of Digital Convergence, 13(1), 439-447. https://doi.org/10.14400/JDC.2015.13.1.439