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Trend Analysis by Using Text Mining of Journal Articles Regarding Consumer Policy
New Physics: Sae Mulli 2017; 67: 555~561
Published online May 31, 2017;
© 2017 New Physics: Sae Mulli.

Min-Jeong KIM, Kyungyoung OHK*, Chung-Sook MOON

Department of Consumer Economics, Sookmyung Women’s University, Seoul 04310, Korea
Correspondence to:
Received January 10, 2017; Revised April 3, 2017; Accepted April 4, 2017.
cc This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper identifies the direction of change in consumer policy research, where consumer policies are shifting from consumer protection perspectives to consumer responsibility perspectives. In this research, text mining and association-rule techniques were used on the titles and keywords of articles published in “Consumer Policy and Education Review”, a major journal for consumer policy studies, from 2005 to 2015 to find major research trends, longitudinal trends, and a research field association rule; these results were visualized so that they could be easily understood. First, 1026 keywords were extracted from 238 papers that had titles and keywords. From these, 73 keywords that appeared 10 times or more were selected as research targets and were analyzed. From the analysis, the longitudinal patterns of the keywords' appearance frequency by year showed steady increasing, increasing, one-time peak, and rapidly increasing then decreasing trends. Finally, the association rule was used to find frequent patterns in the keyword database; this revealed a structure in which groups were formed based on the keyword “consumer”. Through this research, the trends and the keywords of papers on consumer policy studies could be understood; thus, this research can be used as fundamental data for predicting trends in articles on consumer policy studies.
PACS numbers: 89.70.+c, 89.90.+n1
Keywords: Consumer policy’s trend, Text mining, K-means clustering, Association rule

October 2018, 68 (10)
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