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http://dx.doi.org/10.9723/jksiis.2015.20.2.113

A study on unstructured text mining algorithm through R programming based on data dictionary  

Lee, Jong Hwa (부경대학교 일반대학원)
Lee, Hyun-Kyu (부경대학교 경영대학)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.20, no.2, 2015 , pp. 113-124 More about this Journal
Abstract
Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.
Keywords
R program; Big data; Text Mining; unstructured data;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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