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http://dx.doi.org/10.7465/jkdi.2013.24.6.1429

Analysis of the abstracts of research articles in food related to climate change using a text-mining algorithm  

Bae, Kyu Yong (Department of Statistics, Dongguk University-Seoul)
Park, Ju-Hyun (Department of Statistics, Dongguk University-Seoul)
Kim, Jeong Seon (Health Policy Research Department, Korea Institute for Health and Social Affairs)
Lee, Yung-Seop (Department of Statistics, Dongguk University-Seoul)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.6, 2013 , pp. 1429-1437 More about this Journal
Abstract
Research articles in food related to climate change were analyzed by implementing a text-mining algorithm, which is one of nonstructural data analysis tools in big data analysis with a focus on frequencies of terms appearing in the abstracts. As a first step, a term-document matrix was established, followed by implementing a hierarchical clustering algorithm based on dissimilarities among the selected terms and expertise in the field to classify the documents under consideration into a few labeled groups. Through this research, we were able to find out important topics appearing in the field of food related to climate change and their trends over past years. It is expected that the results of the article can be utilized for future research to make systematic responses and adaptation to climate change.
Keywords
Climate change; document classification; hierarchical clustering; text-mining;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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1 Baek, H., Cho, C., Kwon, W., Kim, S., Cho, J. and Kim, Y. (2011). Development strategy for new climate change scenarios based on RCP. Journal of Climate Change Research, 2, 55-68.
2 Cho, S. and Kim, S. (2012). Finding meaningful pattern of key words in IIE transactions using text mining. Journal of the Korean Institute of Industrial Engineers, 38, 67-73.   과학기술학회마을   DOI   ScienceOn
3 Choi, K. and Lee, Y. (2011). The deduction of objective linguistic information using statistical methods - The grouping of the possibility of interdisciplinary research. Journal of the Korean Data & Information Science Society, 22, 49-55.   과학기술학회마을
4 Feinerer, I., Hornik, K. and Meyer, D. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25, 1-54.
5 Feinerer, I. (2013). Introduction to the tm package text mining in R, R News, http://CRAN.R-project.org/doc/Rnews/.
6 Go, G., Jung, W., Shin, Y., Park, S. and Jang, D. (2011). A study on development of patent information retrieval using text mining, Journal of the Korea Academia-Industrial Cooperation Society, 12, 3677-3688.   DOI   ScienceOn
7 Kim, J. and Jeong, C. (2012). Analysis of trend in construction using text mining method. Journal of The Korean Digital Architecture·Interior Association, 12, 53-60.
8 Lim, J. and Lim, D. (2012). Comparison of clustering methods of microarray gene expression data. Journal of the Korean Data & Information Science Society, 23, 39-51.   과학기술학회마을   DOI   ScienceOn
9 Rousseeuw, P. J. (1987). Silhouettes : Graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 54-65.
10 Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M. and Miller, H. L. (2007). Climate change 2007, Cambridge University Press, Cambridge, United Kingdom, 996.
11 Yeo, I. (2011). Clustering analysis of Korea's meteorological data. Journal of the Korean Data & Information Science Society, 22, 941-949.   과학기술학회마을