Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

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

텍스트 마이닝 기법을 활용한 기후변화관련 식품분야 논문초록 분석

  • 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)
  • 배규용 (동국대학교 통계학과) ;
  • 박주현 (동국대학교 통계학과) ;
  • 김정선 (한국보건사회연구원 보건정책연구본부) ;
  • 이영섭 (동국대학교 통계학과)
  • Received : 2013.10.22
  • Accepted : 2013.11.19
  • Published : 2013.11.30
Download PDF
⟨ Previous Next ⟩

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.

빅 데이터 분석기법 중 비정형데이터 분석기법인 텍스트 마이닝 기법을 이용하여 기후변화 관련 식품분야 논문 초록에서 용어들의 출현빈도를 분석하였다. 이를 위하여 용어-문헌 행렬을 만들고, 용어들간의 비유사성 측도를 바탕으로 계층적 군집분석기법을 적용하여 문서들을 군집화하였다. 군집화된 문서들간의 상호 연관성과 군집별로 특정용어의 빈도를 파악하여 문서군집을 특정주제별로 분류하였다. 이러한 연구를 통하여 식품분야의 기후변화 관련 논문들의 추세와 관심주제어를 파악할 수 있었으며, 향후 기후변화 적응 및 대응 체계 로드맵 작성 시 연구 개발 기초 자료로 활용할 수 있을 것이다.

Keywords

References

  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. https://doi.org/10.7232/JKIIE.2012.38.1.067
  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.
  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. https://doi.org/10.5762/KAIS.2011.12.8.3677
  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. https://doi.org/10.7465/jkdi.2012.23.1.039
  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.

Cited by

  1. Selecting order of priority using Delphi and statistical method vol.25, pp.6, 2014, https://doi.org/10.7465/jkdi.2014.25.6.1161
  2. A Study on the Research Trends in the Area of Geospatial-Information Using Text-mining Technique Focused on National R&D Reports and Theses vol.22, pp.4, 2014, https://doi.org/10.12672/ksis.2014.22.4.011
  3. Classification of ratings in online reviews vol.27, pp.4, 2016, https://doi.org/10.7465/jkdi.2016.27.4.845
  4. Analysis of patterns in meteorological research and development using a text-mining algorithm vol.29, pp.5, 2016, https://doi.org/10.5351/KJAS.2016.29.5.935
  5. A meta analysis of the climate change impact on rice yield in South Korea vol.26, pp.2, 2015, https://doi.org/10.7465/jkdi.2015.26.2.355
  6. Study on prediction for a film success using text mining vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1259
  7. Analysis of statistical models on temperature at the Suwon city in Korea vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1409
  8. Analysis of statistical models on temperature at the Seosan city in Korea vol.25, pp.6, 2014, https://doi.org/10.7465/jkdi.2014.25.6.1293
  9. Study on the Trends of U-City and Smart City Researches using Text Mining Technology vol.22, pp.3, 2014, https://doi.org/10.7319/kogsis.2014.22.3.087
  10. R을 활용한 정보교육관련 논문 분석 vol.21, pp.1, 2013, https://doi.org/10.14352/jkaie.2017.21.1.57
  11. 텍스트마이닝을 활용한 숭례문 관련 기사의 트렌드 분석 vol.17, pp.3, 2013, https://doi.org/10.5392/jkca.2017.17.03.474
  12. 텍스트 마이닝 기법을 활용한 환경공간정보 연구 동향 분석 vol.20, pp.1, 2017, https://doi.org/10.11108/kagis.2017.20.1.113
  13. 텍스트 마이닝 기법을 이용한 환경 분야의 ICT 활용 연구 동향 분석 vol.33, pp.2, 2013, https://doi.org/10.7780/kjrs.2017.33.2.7
  14. 고객센터를 통한 고객지식 확보 전략: 음성인식기술의 적용 사례 vol.19, pp.1, 2018, https://doi.org/10.15813/kmr.2018.19.1.009