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http://dx.doi.org/10.6109/jkiice.2021.25.9.1199

Text Mining and Visualization of Unstructured Data Using Big Data Analytical Tool R  

Nam, Soo-Tai (Institute of General Education, Pusan National University)
Shin, Seong-Yoon (School of Computer Information & Communication Engineering, Kunsan National University)
Jin, Chan-Yong (Division of Information & Electronic Commerce, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.9, 2021 , pp. 1199-1205 More about this Journal
Abstract
In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.
Keywords
Network analysis; Unstructured data; Big data; Association analysis; Text mining;
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