한국IT서비스학회지 (Journal of Information Technology Services)

한국IT서비스학회 (Korea Society of IT Services)
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토픽 모델링을 이용한 방송미디어 관련 소셜 미디어 콘텐츠 분석

Analysis of Social Media Contents about Broadcast Media through Topic Modeling

  • 박상언 (경기대학교 경상대학 경영정보학과)
  • 투고 : 2016.01.30
  • 심사 : 2016.05.27
  • 발행 : 2016.06.30
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초록

Numerous people share their TV experience with other viewers on social media such as personal blogs and Twitter. It means that broadcast media, especially TV, affects the responses on social media. Moreover, the responses affect broadcast media ratings back. Social TV tried to use the relationship in marketing activities such as advertisement by analyzing the TV related social behavior. However, most of them used just the quantities of social media responses. This study analyzes the subjects of the responses on social media about specific TV dramas through topic modeling, and the relationship between the changes of popular topics and viewer ratings of the drama over specified periods. Five representative Korean dramas of 2014 were selected and Blog contents including viewer ratings about the dramas were collected from naver.com which is the representative portal in South Korea. The proposed analysis framework consists of three steps which are Blogs crawling, topic modeling, and topic trend analysis. We found some implications from the results of the topic trend analysis. Firstly, there were specific topics on dramas in social media. Secondly, the topics had some meaningful relationships with viewer ratings. Lastly, there were differences between the topics of dramas with higher viewer ratings and those with lower viewer ratings.

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참고문헌

  1. Bae, J.H., N.G. Han, and S. Min, "Twitter Issue Tracking System by Topic Modeling Techniques", Journal of Intelligence and Information Systems, Vol.20, No.2, 2014, 109-122. https://doi.org/10.13088/JIIS.2014.20.2.109
  2. Blei, D., L. Carin, and D. Dunson, "Probabilistic Topic Models", Signal Processing Magazine, IEEE, Vol.27, No.6, 2010, 55-65. https://doi.org/10.1109/MSP.2009.934715
  3. Blei, D.M. and J.D. Lafferty, "Topic Models", Text mining : Classification, Clustering, and Applications, Vol.10, No.71, 2009, 34.
  4. Blei, D.M., A.Y. Ng, and M.I. Jordan, "Latent Dirichlet Allocation", the Journal of Machine Learning Research, Vol.3, 2003, 993-1022.
  5. Bolelli, L., S. Ertekin, D. Zhou, and C.L. Giles, "Finding Topic Trends in Digital Libraries", Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital libraries, 2009.
  6. Brown, A., "Big Data, Big Opportunity?", available at http://www.broadcastnow.co.uk/techfacils/ibc-2013/big-data-big-opportunity/5060598.article, 2013.
  7. Carr, D., "Giving Viewers What They Want", The New York Times, available at http://www.nytimes.com/2013/02/25/business/media/f or-house-of-cards-using-big-data-to-guarantee-its-popularity.html?_r=02013.
  8. Cesar, P. and D. Geerts, "Past, Present, and Future of Social Tv : A Categorization", Proceedings of Consumer Communications and Networking Conference (CCNC), 2011 IEEE, 2011a.
  9. Cesar, P. and D. Geerts, "Understanding Social Tv : A Survey", Proceedings of the Networked and Electronic Media Summit(NEM Summit 2011), Torino, Italy, 2011b, 94-99.
  10. Deb, R., "Social Tv", TVB, available at http://www.tvb.ca/pages/deb+roy+tv+day+2012_htm, 2012.
  11. Dmc_Marketing_Team, "2014 Sns Usage Report", DMC Media, available at http://www.slideshare.net/BeckyKim/2014-sns-37219822, 2014.
  12. Hyun, Y.J., N.G. Kim, and Y.H. Cho, A Multi-Dimensional Issue Clusteringfrom the Perspective Consumers' Interests and R&D, Journal of Information Technology Services, Vol.14, No.1, 2015, 237-249. (현윤진, 김남규, 조윤호, "소비자 선호 이슈 및 R&D 관점에서의 다차원 이슈 클러스터링", 한국IT서비스학회지, 제14권, 제1호, 2015, 237-249.)
  13. Jung, H.I., "Economic Ripple Effect of 'My Love from the Star' ", Cultural Tourism, available at https://www.kcti.re.kr/webzine2/webzineView.action?issue_count=32&menu_seq=4 &board_seq=1, 2014. (정헌일, "드라마 '별에서 온 그대'의 경제적 파급효과", 문화관광, No.7, 2014.)
  14. Kim, W.J., J.H. Won, S.U. Park, and J.Y. Kang, "Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis", International Journal of Distributed Sensor Networks, Vol.2015, No.1, 2015, 1-6.
  15. KLT, "Klt : Korean Language Technology", available at http://nlp.kookmin.ac.kr/HAM/kor/, 2014.
  16. Kushin, M.J. and M. Yamamoto, "Did Social Media Really Matter? College Students' Use of Online Media and Political Decision Making in the 2008 Election", Mass Communication and Society, Vol.13, No.5, 2010, 608-630. https://doi.org/10.1080/15205436.2010.516863
  17. Lee, H., 2014 Drama Rank, NEWSPIM, available at http://www.newspim.com/view.jsp?newsId=20141222000007 2014.
  18. Martie, L., V.K. Palepu, H. Sajnani, and C. Lopes, "Trendy Bugs : Topic Trends in the Android Bug Reports", Proceedings of the 9th IEEE Working Conference on Mining Software Repositories, 2012.
  19. Matsubara, Y., Y. Sakurai, C. Faloutsos, T. Iwata, and M. Yoshikawa, "Fast Mining and Forecasting of Complex Time-Stamped Events", Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012.
  20. Michaelidou, N., N.T. Siamagka, and G. Christodoulides, "Usage, Barriers and Measurement of Social Media Marketing : An Exploratory Investigation of Small and Medium B2b Brands", Industrial Marketing Management, Vol.40, No.7, 2011, 1153-1159. https://doi.org/10.1016/j.indmarman.2011.09.009
  21. Mimno, D., "Tutorial : Machine Learning with Mallet", available at http://mallet.cs.umass.edu/mallet-tutorial.pdf, 2013.
  22. Park, S.U., J.Y. Kang, Y.S. Bang, and K.S. Han, "Relationship Between Audience Engagement On Social Media And Broadcast Media-Ratings", Proceedings of the 15th International Conference on Electronic Business, 2015.
  23. Roettgers, J., "Let's Face It : Social Tv Is Dead", GIGAOM, available at https://gigaom.com/2014/01/29/lets-face-it-social-tv-is-dead, 2014.