Research on Methods for Processing Nonstandard Korean Words on Social Network Services

Title & Authors
Research on Methods for Processing Nonstandard Korean Words on Social Network Services
Lee, Jong-Hwa; Le, Hoanh Su; Lee, Hyun-Kyu;

Abstract
Social network services (SNS) that help to build relationship network and share a particular interest or activity freely according to their interests by posting comments, photos, videos,$\small{{\ldots}}$ on online communities such as blogs have adopted and developed widely as a social phenomenon. Several researches have been done to explore the pattern and valuable information in social networks data via text mining such as opinion mining and semantic analysis. For improving the efficiency of text mining, keyword-based approach have been applied but most of researchers argued the limitations of the rules of Korean orthography. This research aims to construct a database of non-standard Korean words which are difficulty in data mining such abbreviations, slangs, strange expressions, emoticons in order to improve the limitations in keyword-based text mining techniques. Based on the study of subjective opinions about specific topics on blogs, this research extracted non-standard words that were found useful in text mining process.
Keywords
Text Mining;Non-standard;Stemming Korean;Unicode;
Language
Korean
Cited by
References
1.
Lee, J. H., "Big Data, Data Mining and Temporary Reproduction," The Journal of Intellectual Property, Vol. 8, No. 4, 2013, pp. 93-125.

2.
Kang, S. J., "Constructing a Large Interlinked Ontology Network for the Web of Data," Journal of Korean Industrial Information Systems Society, Vol. 15, No. 1, 2010, pp. 15-23.

3.
Park, C. S., Hong, Y. J. and Cho, I. H., "An Analysis on Journalism Characteristics of SNS based on Issued Cases : With Twitter as the Center," Proceedings in 2012 Fall Conference of The Korean Entertainment Industry Association, 2012, pp. 36-40.

4.
Boyd, D. M. and Ellison, N. B., "Social Network Sites: Definition, History, and Scholarship," Journal of Computer-Mediated Communication, Vol. 13, No. 4, 2007, pp. 210-230.

5.
Kim, W. S., Lee, J. H., Park, j. W. and Choi, j. H.,"A Technique of the Approval Rating Analysis for Political Party Using Opinion Mining,", Journal of Korean Institute of Information Technology, Vol. 12, No. 10, 2014, pp. 133-141.

6.
Won, J. Y. and Kim, D. G., "Deduction of Social Risk Issues Using Text Mining," Journal of safety and crisis management, Vol. 10, No. 7, 2014, pp. 33-52.

7.
Lee, J. H. and Lee, H. K., "A Study on Unstructured Text Mining Algorithm through R Programming based on Data Dictionary," Journal of the Korea Society Industrial Information System, Vol. 20, No. 2, 2015, pp. 113-124.

8.
Chang, J. Y., Lee, s. Y. and Han, J. B., "Machine-Learned Classification Technique for Opinion Documents Retrieval in Social Network Services," Proceedings in 2013 Conference of Korean Institute of Information Scientists and Engineers, 2013, pp. 245-247.

9.
Chang, C. Y., Jang, J. H., Kim, S, H., Lee, H. K. and Lee, C. H., "A Study on the Efficient Patent Search Process using Big Data Analysis Tool R," Journal of Korea Safety Management & Science, Vol. 15, No. 4, 2013, pp. 289-294.

10.
Le, H., and Lee, H. K., "Exploring Relationship Between Social ICT Issues And Academic Research Interests Through Text Mining Analysis," The Journal of Internet Electronic Commerce Research, Vol. 14, No. 5, 2014, pp. 161-180.

11.
Le, H., Lee, J. H. and Lee, H. K., "Purchase Process Aspect-based Opinion Mining : An Application for Online Shopping Mall," The Journal of Internet Electronic Commerce Research, Vol. 15, No. 2, 2015, pp. 15-28.

12.
Yun, B. H., "Natural Language Processing based Information Extraction for Newspapers," Journal of Korean Institute of Information Technology, Vol. 6, No. 4, 2008, pp. 188-195.

13.
Hong, J. P. and Cha, J. W., "Error Correction of Sejong Morphological Annotation Corpora using Part-of-Speech Tagger andFrequency Information," Journal of KISS : Software and Applications, 2013, Vol. 40, No. 7, pp. 417-428.

14.
Sim, K. S., "Syllable-based POS Tagging without Korean Morphological Analysis," Korean Journal of Cognitive Science, Vol. 22, No. 3, 2011, pp. 327-345.

15.
An, J. K. and Kim, H. U., "Building a Korean Sentiment Dictionary and Applications of Natural Language Processing," Proceedings in 2014 Summer Conference of Korea Intelligent Information Systems Society, 2014, pp. 177-182.

16.
Kwon H. R., Na J. H., Yoo J. S. and Cho W. S., "Text-mining Techniques for Metabolic Pathway Reconstruction," Journal of Korean Industrial Information Systems Society, Vol. 12, No. 4, pp. 138-147, 2007.

17.
URL http://www.korean.go.kr/

18.
URL http://www.naver.com/

19.
URL http://www.unicode.org/