Browse > Article
http://dx.doi.org/10.6109/jkiice.2017.21.8.1567

Analysis of drama viewership related words through unstructured data collection  

Kang, Sun-Kyoung (Department of Computer Software Engineering, Wonkwang University)
Lee, Hyun-Chang (Department of Digital Contents Engineering, Wonkwang University)
Shin, Seong-Yoon (School of Computer Information & Communication Engineering, Kunsan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.8, 2017 , pp. 1567-1574 More about this Journal
Abstract
In this paper, we analyzed the stereotyped and non - stereotyped data in order to analyze the drama 's ratings. The formalized data collection collected 19 items from the four areas of drama information, person information, broadcasting information, and audience rating information of each broadcasting company. Atypical data were collected from bulletin boards, pre - broadcast blogs and post - broadcast blogs operated by each broadcasting company using a crawling technique. As a result of comparing the differences according to the four areas for each broadcaster from the collected regular data, the results were similar to each other. And we derived seven related words by analyzing the correlation of occurrence frequencies from unstructured data collected from bulletin boards and blogs of each broadcasting company. The derived associations were obtained through reliability analysis.
Keywords
Structured data; Unstructured data; related words; crawling; Text mining;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
연도 인용수 순위
1 S. K Kang, H. C Lee, and S. Y Shin, "Analysis of related words of drama viewership through SNS unstructured data crawling," in Proceedings of the 41th Annual Conference of Korea Institute of information and Communication, pp. 105, June 2017.
2 S. H Yun, K. H Lee, H. S Lim, D.R Kim, and J. H Kim, "The Method of Digital Copyright Authentication for Contents of Collective Intelligence," Journal of the Korea Convergence Society, vol. 6, no. 6, pp. 185-193, June 2015.   DOI
3 Y. W. No, D. Y. Kim, J. E, Han, J. T. Lim, K. S. Boek, and J. S. Yoo, "Hot Topic Prediction Scheme Considering User Influences in Social Networks", Journal of Korea Contents Association Research, vol.15, no. 8, pp24-36, Dec. 2015.
4 J. B. Lee, C. K. Lee, and K. J. Cha, "An Analysis of IT Trends Using Tweet Data", Journal of Intelligence and Information Systems, vol. 21, no. 1, pp143-159, Sep. 2015.   DOI
5 Y. J Lee, J. H Seo, and J. T Choi, "Fashion Trend Marketing Prediction Analysis Based on Opinion Mining Applying SNS Text Contents," Journal of KIIT. vol. 12, no. 12, pp. 163-170, June 2014.
6 H. J Kim and J. Y Chang, "Discovering News Keyword Associations Using Association Rule Mining," The Journal of the Institute of Internet Broadcasting and Communication, vol. 11, no. 6, pp. 63-71, Dec. 2011.
7 B. W. Kim, "Trend Analysis and National Policy for Artificial Intelligence." Informatization Policy, vol. 23, no. 1, pp. 74-93, Mar. 2016.   DOI
8 J. Y. Seo and C. Koh, "Big Data Analysis by Sensitivity Analysis," Journal of the Society of Convergence Knowledge, vol. 2, no. 1, pp. 15-21, June 2014.
9 J. S. Sohn, S. W. Cho, K. L. Kwon, and I. J. Chung, "Improved Social Network Analysis Method in SNS," Journal of Intelligence and Information Systems, vol. 18, no. 4, pp. 117-127, Mar. 2012.   DOI
10 S. N. Kang, Y. S. Kim, S. H. Choi, "Study on the social issue sentiment classification using text mining." Journal of the Korean data & information science society, vol. 26, no. 5, pp. 1167-1173, June 2015.   DOI
11 J. Y. Jang, "Automatic Retrieval of SNS Opinion Document Using Machine Learning Technique.," The Journal of the Institute of Internet, Broadcasting and Communication, vol. 13, no. 5, pp. 27-35, Sep. 2013.   DOI