Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Product Community Analysis Using Opinion Mining and Network Analysis: Movie Performance Prediction Case

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

  • Jin, Yu (Dept. of Business Administration, Graduate School, Hanyang University) ;
  • Kim, Jungsoo (Dept. of Business Administration, Graduate School, Hanyang University) ;
  • Kim, Jongwoo (School of Business, Hanyang University)
  • 진위 (한양대학교 일반대학원 경영학과) ;
  • 김정수 (한양대학교 일반대학원 경영학과) ;
  • 김종우 (한양대학교 경영대학 경영학부)
  • Received : 2014.02.06
  • Accepted : 2014.03.06
  • Published : 2014.03.28
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Abstract

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

구전(WOM: Word of Mouth)는 주변 사람들에게 상품에 대한 경험을 입에서 입으로 전달하는 현상을 말하며 소셜 미디어의 발전으로 온라인 구전(eWOM: Electronic Word of Mouth) 형태로 발전하였다. 구전 효과의 중요성으로 인해서 대부분의 기업들의 자사의 상품이나 서비스에 대한 온라인 구전에 촉각을 세우고 있으며, 특히 영화와 같은 경험재의 경우에는 그 영향력이 더욱 크다. 본 연구에서는 영화 커뮤니티에 대한 사회 네트워크 분석을 통해서 영화 흥행성과 지표인 매출에 미치는 영향요인을 규명하고자 한다. 영화 흥행성과 연구들에서 주요하게 다루어진 영화에 대한 구전의 크기(volume)와 방향성(valence)과 같은 구전 요인들을 추가하여, 구전 네트워크의 중심성 척도를 영향 요인에 고려하였다. 구전의 크기, 방향성, 그리고 3가지 중심성 척도(연결 중심성, 매개 중심성, 근접 중심성)의 최종 영화 매출에 영향 관계를 가설로 설정하였다. 제시한 연구 모형을 검증하기 위하여 대표적인 온라인 영화 커뮤니티 사이트인 IMDb(Internet Movie Database)에서 영화 구전 데이터를 수집하였고, Box-Office-Mojo사이트에서 영화 매출 데이터를 수집하였다. 2012년 9월부터 1년 동안, 주간 Top-10에 포함된 적이 있는 영화들을 대상으로 하였으며, 총 103개의 영화가 선정되어 이 영화들에 대한 메타 데이터와 커뮤니티 데이터가 수집되었다. 영화 커뮤니티 네트워크는 평가자들간의 댓글 관계를 기초로 구축하였다. 본 연구에서 사용한 3가지 중심성 척도는 사회 네트워크 분석 도구인 NodeXL을 사용하여 계산되었으며, 각 영화별 커뮤니티 참여자들의 중심성 척도의 평균값을 활용하였다. 가설 검증의 사전 분석을 위한 상관관계 분석에서는 3가지 중심성 척도간에 상관 관계가 높은 것으로 파악되어서, 각각에 대하여 별도로 회귀분석을 수행하였다. 분석 결과, 기존 연구와 일관성 있게 구전의 크기와 방향성은 영화 성과지표인 최종 매출에 긍정적인 영향을 미치는 것으로 파악되었다. 또한 구전 네트워크 내의 참여자 매개중심성 평균은 영화의 최종 매출에 영향을 미치는 것으로 파악되었다. 하지만 연결중심성과 근접중심성은 최종 매출에 영향을 주지 못하는 것으로 나타났다.

Keywords

References

  1. Aaker, D. and J. Myers, Advertising Management 2nd edition, Prentice-Hall, 1982
  2. Ahn, S. A. and T. J. Kim, "The Determinants of Opening Share and Decay Rate in Motion Pictures," Korean Marketing Association, Vol.18, No.3(2003), 1-17
  3. Bae, J. H., B. J. Shim, and B. D. Kim, "Simultaneous Effect between eWOM and Revenues: Korea Movie Industry," Korean Journal of Marketing Association, Vol.12, No.2(2010), 1-25
  4. Balkudi, P. and D. A. Harrison, "Ties, Leaders and Time in Tearms: Strong Inference about Network Structure's Effects on Team Viability and Performance," The Academy of Management Journal, Vol.49, No.1(2006), 49-68 https://doi.org/10.5465/AMJ.2006.20785500
  5. Barnett, G. A. and J. A. Danowski, "The Structure of Communication: A Network Analysis of the International Communication Association," Human Communication Research, Vol.19, No.2(1992), 264-285
  6. Broderick, A. J., J. Brown, and N. Lee, "Wordof-mouth Communication within Online Communities: Conceptualizing the Online Social Network," Journal of Interactive Marketing, Vol.21, No.3(2007), 2-20
  7. Chen, P. Y., S. Y. Wu, and J. Yoon, "The Impact of Online Recommendations and Consumer Feedback on Sales," Proceedings of the International Conference on Information System, (2004).
  8. Cho, Y. H. and J. H. Bang, "Social Network Analysis for New Product Recommendation," Journal of Intelligence and Information Systems, Vol.15, No.4(2009), 183-200
  9. Duan, W., B. Gu, and A. Whinston, "Do Online Reviews Matter? An Empirical Investigation of Panel Data," Decision Support Systems, Vol.45, No.4(2008), 1007-1016 https://doi.org/10.1016/j.dss.2008.04.001
  10. Duan, W., B. Gu, and A. Whinston, "The Dynamics of Online Word-of-month and Product Sales - An Empirical Investigation of the Movie Industry." Journal of Retailing, Vol.84, No.2(2008), 233-242 https://doi.org/10.1016/j.jretai.2008.04.005
  11. Eliashberg, J. and M. S. Shugan, "Film Critics: Influencers or Predictors?," Journal of Marketing, Vol.61, No.2(1997), 68-78
  12. Faulkner, R. R, Music on Demand: Composers and careers in the Hollywood Film Industry, Transaction Books, New Brunswick, NJ, 1987
  13. Jin, Y., Network Diffusion Factors Affecting Movie Performance Based on Online Word-of- Mouth, MS Thesis, Hanyang University, 2013
  14. Jun, South Korea Film Industry Size and Growth Factors Predictive Analysis Report, Korean Film Council, 2004
  15. Jung, H. W, "Complex Networks and Smart World," Conference of Korea Association of Theoretical Sociology, (2003)
  16. Jung, Y. H., "The Influence of Characteristics (Volume, Consensus, Informant) of Electric Word-of-Mouth on Customers' Choice," Broadcasting and Communication, Vol.14, No.1(2013), 47-76
  17. Kim, H. K., J. K. Kim, and Q. Y. Chen, "A Network Approach to Derive Product Relations and Analyze Topological Characteristics," Journal of Intelligence and Information Systems, Vol.15, No.4(2009), 159-182
  18. Kim, J. S. and J. W. Kim, "Relationship between Internet Buzz Share and Markets Share: Movie Ticket Case," The Journal of Society for e-Business Studies, Vol.14, No.2(2013), 242-255
  19. Kim, J. W., "Social Network Approach for Organizational Research," Entrue Journal of Information Technology, Vol.11, No.1(2012), 29-43
  20. Kim, S. H., "The Influence of Product Involvement and Knowledge on Internet WOM," The Korean Journal of Advertising, Vol.14, No.1(2003), 257-280
  21. Lee, J. C, "The Influence on the Moviegoer Perception of Movie Selection by the Marketing Variables," Film Studies Association of Korea, No.23(2004), 293-330
  22. Lee, J. Y., I. S. Son, and D. W. Lee, "Does Online Social Network Contribute to WOM Effect on Product Sales?," Journal of Intelligence and Information Systems, Vol.18, No.2(2012), 85-106
  23. Lee, T. M. and C. Park, "Effects of Direction and Type of Electronic Word of Mouth Information on Purchase Decision: Cross- Cultural Comparison Between Korea and U.S.," Korean Marketing Association, Vol.21, No.1(2006), 29-56
  24. Linton, J. and J. Petrovich, "The Application of the Consumer Information Acquisition Approach to Movie Selection," Current Research in Film, Vol.4(1988), 24-44
  25. Litman, B. R. and L. S. Kohl, "Predicting Financial Success of Motion Pictures: The Early '80s Experience," Journal of Media Economics, Vol.2, No.2(1989), 35-50 https://doi.org/10.1080/08997768909358184
  26. Litman, B. R., "Predicting Success of Theatrical Movies: An Empirical Study," Journal of Popular Culture, Vol.16, No.4(1983), 159-175 https://doi.org/10.1111/j.0022-3840.1983.1604_159.x
  27. Liu, Y., "Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue," Journal of Marketing, Vol.70, No.3(2006), 74-89 https://doi.org/10.1509/jmkg.70.3.74
  28. Mahajan, V., E. Muller, and R. Kerin, "Introduction Strategy for New Products with Positive and Negative Word-of-mouth," Management Science, Vol.30, No.12(1984), 1389-1404 https://doi.org/10.1287/mnsc.30.12.1389
  29. Mizerski, R., "An Attribution Explanation of the Disproportionate Influence of Unfavorable Information," Journal of Consumer Research, Vol.9, No.3(1982), 301-310
  30. Mizruchi, M. S., "Social Network Analysis: Recent Achievements and Current Controversies," Acta Sociologica, Vol.37(1994), 327-343 https://doi.org/10.1177/000169939403700402
  31. Money, R. B., M. C. Gilly, and J. L. Graham, "Explorations of National Culture and Word-of-mouth Referral Behavior in the Purchase of Industrial Services in the United States and Japan," Journal of Marketing, Vol.62, No.4(1998), 76-87 https://doi.org/10.2307/1252288
  32. Moon, S., P. Bergey, and D. Lacobucci, "Dynamic Effects among Movie Ratings, Movie Revenues, and Viewer Satisfaction," Journal of Marketing, Vol.74, No.1(2010), 108-121 https://doi.org/10.1509/jmkg.74.1.108
  33. Naan, S. J., M. Krauss, P. A. Schober, K. Gloor, H. F. Fischbach, "Comparing the Structure of Virtual Entrepreneur Networks with Business Effectiveness," Procedia-Social and Behavioral Sciences, Vol.2, No.4 (2010), 6483-6496 https://doi.org/10.1016/j.sbspro.2010.04.058
  34. Opsahl, T., F. Agneessens, and J. Skvoretz, "Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths," Social Networks, Vol.32, No.3(2010), 245-251 https://doi.org/10.1016/j.socnet.2010.03.006
  35. Park, H. H. and C. S. Park, "The Relationship between Critical Reviews and Performance of Movies: Does it Hold in the Internet Era?," Korean Marketing Association, Vol.16, No.4 (2001), 71-85
  36. Park, J. H. and B. M. Suh, "The Impact of Influential's Betweenness Centrality on the WOM Effect under the Online Social Networking Service Environment," Journal of Information Technology Applications & Management, Vol.20, No.2(2013), 127-146
  37. Park, J. W. and G. O. Lee, "A Study on the Effects of the Newspaper Coverage of Motion Pictures on Box Office Performance," Korean Journal of Journalism and Communication Studies, Vol.48, No.6(2004), 62-83
  38. Park, S. and E. H. Shin, "Patterns of Market Polarization and Market Matching in the Korean Film Industry," Journal of East Asian Studies, Vol.4(2004), 285-300 https://doi.org/10.1017/S1598240800001843
  39. Powell, W. W., K. W. Koput, and L. Smith-Doerr, "Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology," Administrative Science Quarterly, Vol.41, No.1(1996), 116-145 https://doi.org/10.2307/2393988
  40. Rogers, Diffusion of innovations, Free Press, 2003
  41. Senecal, S. and J. Nantel, "The Influence of Online Product Recommendations on Consumers' Online Choices," Journal of Retailing, Vol.80, No.2(2004), 159-169 https://doi.org/10.1016/j.jretai.2004.04.001
  42. Sung, Y. S., J. Y. Park, and E. A. Park, "The Influence of On-line Word of Mouth Information On Viewing Intention toward Motion Picture," Korean Association For Advertising Public Relations, No.57(2002), 31-52
  43. Yang, H. L. and J. H. Tang, "Team Structure and Team Performance in IS Development: A Social Network Perspective," Information and Management, Vol.41, No. 3(2004), 335-349 https://doi.org/10.1016/S0378-7206(03)00078-8
  44. Yi, Y. J. and C. L. Lee, "The Dynamic Effects of Customer Satisfaction and Referral on Firm Performance: Focusing on KCSI and KNPS," The Korean Academic Society of Business Administration, Vol.39, No.2(2010), 279-307

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