JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Study on prediction for a film success using text mining
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Study on prediction for a film success using text mining
Lee, Sanghun; Cho, Jangsik; Kang, Changwan; Choi, Seungbae;
  PDF(new window)
 Abstract
Recently, big data is positioning as a keyword in the academic circles. And usefulness of big data is carried into government, a local public body and enterprise as well as academic circles. Also they are endeavoring to obtain useful information in big data. This research mainly deals with analyses of box office success or failure of films using text mining. For data, it used a portal site `D` and film review data, grade point average and the number of screens gained from the Korean Film Commission. The purpose of this paper is to propose a model to predict whether a film is success or not using these data. As a result of analysis, the correct classification rate by the prediction model method proposed in this paper is obtained 95.74%.
 Keywords
Correct classification rate;opinion mining;singular value decomposition;text mining;
 Language
Korean
 Cited by
 References
1.
An, S. W. and Cho, S. B. (2010). Stock prediction using news text mining and time series analysis. Journal of Computing Science and Engineering, 37, 77-82.

2.
Bae, K. Y., Park, J. H., Kim, J. S. and Lee, Y. S. (2013). Analysis of the abstracts of research articles in food related to climate change using a text-mining algorithm. Journal of the Korean Data & Information Science Society, 24, 1429-1437. crossref(new window)

3.
Baek, G. I., Kim, K. K., Choi, S. B. and Kang, C. W. (2015). Prediction for the Films Success using Stylometry. Journal of the Korean Data Analysis Society, 17, 719-728.

4.
Chun, H. J. and Leem, B. H. (2014). Face/non-face channel fit comparison of life insurance company and non-life insurance company using social network analysis. Journal of the Korean Data & Information Science Society, 25, 1207-1219. crossref(new window)

5.
Jung, K. H. (2010). A study of foresight method based on text mining and complexity network analysis. Korea Institute of S&T Evaluation and Planning, Seoul.

6.
Kang, B. U., Huh, M. K. and Choi, S. B. (2015). Performance analysis of volleyball games using the social network and text mining techniques. Journal of the Korean Data & Information Science Society, 26, 1-12. crossref(new window)

7.
Kim, K. H. and Oh, S. Y. (2009). Methodology for applying text mining techniques to analyzing online customer reviews for market segmentation. International Journal of Contents, 9, 272-284.

8.
Lee. J. Y. and Kim, H. J. (2014). Identification of major risk factors association with respiratory diseases by data mining. Journal of the Korean Data & Information Science Society, 25, 373-384. crossref(new window)

9.
Oh, S. W. and Jin, S. H. (2012). A study on analysis of internet shopping mall customers' reviews by text mining. Journal of the Korean Data Analysis Society, 14, 125-137.

10.
Oh, H. S., Cho, S. K., Kang, C. W. and Lim, D. S. (2010). Fashion Company's Claim Data Analysis Using Text Mining. Journal of the Korean Data Analysis Society, 12, 297-306.

11.
Park, H. W. and Lee, Y. O. (2009). A mixed text analysis of user comments on a portal site : The 'BBK Scandal' in the 2007 presidential election of south korea. Journal of the Korean Data Analysis Society, 11, 731-744.

12.
SAS Korea. (2010). G etting Started with SAS Text Miner 4.2., SAS Siftware Korea Ltd.

13.
Yu, E. J., Kim Y. S., Kim, N. K. and Jung, S. R. (2013). Predicting the direction of the stock index by using a domain-specific sentiment dictionary. Journal of intelligence and information systems, 19, 95-110. crossref(new window)

14.
Yune, H. J., Kim, H. J. and Chang, J. Y. (2010). An efficient search method of product reviews using opinion mining techniques. Journal of Computing Science and Engineering, 16, 222-226.