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A Trend Analysis of Agricultural and Food Marketing Studies Using Text-mining Technique

텍스트마이닝 기법을 이용한 국내 농식품유통 연구동향 분석

  • Received : 2017.07.06
  • Accepted : 2017.10.13
  • Published : 2017.10.31

Abstract

This study analyzed trends in agricultural and food marketing studies from 1984 to 2015 using text-mining techniques. Text-mining is a part of Big-data analysis, which is an effective tool to objectively process large amounts of information based on categorization and trend analysis. In the present study, frequency analysis, topic analysis and association rules were conducted. Titles of agricultural and food marketing studies in four journals and reports were used for placing the analysis. The results showed that 1,126 total theses related to agricultural and food marketing could be categorized into six subjects. There were significant changes in research trends before and after the 2000s. While research before 2000s focused on farm and wholesale level marketing, research after the 2000s mainly covered consumption, (processed)food, exports and imports. Local food and school meals are new subjects that are increasingly being studied. Issues regarding agricultural supply and demand were the only subjects investigated in policy research studies. Interest in agricultural supply and demand was lost after the 2000s. A number of studies after the 2010s analyzed consumption, primarily consumption trends and consumer behavior.

이 연구는 1984~2015년간 국내 농식품 유통분야 연구동향을 파악하기 위해 텍스트마이닝 기법을 이용한 분석 결과이다. 텍스트마이닝은 빅데이터 분석방법의 일환으로, 많은 정보를 객관적으로 처리하여 연구주제 분류와 트렌드 분석에 이용할 수 있다. 실제분석에는 빈도분석, 토픽분석, 연관성분석을 수행하였다. 자료는 농업부문 4개 학술지 수록논문과 연구보고서 중 농식품 유통 관련 연구 제목를 이용하였다. 그 결과, 농식품 유통분야의 논문 1,126건은 6개 주제로 분류되었다. 2000년대를 기점으로 이전에는 도매와 산지연구가 활발했던 반면 이후에는 소비, 식품, 수출입 연구가 활발한 것으로 나타났다. 또한 로컬푸드와 학교급식 영역의 연구가 증가했다. 농산물 수급연구는 정책 연구보고서에서만 주기적으로 이루어졌으며, 학술논문에서는 2000년대 이후 관심주제에서 멀어지는 경향을 보였다. 2010년대 이후로는 특히 소비연구가 주류를 이루었고, 크게 소비트렌드와 소비자 행동에 관한 다양한 연구가 이루어졌다. 이 결과를 바탕으로 더 정확한 연구동향 분석을 하기위해서는, 정밀한 주제 분류기법으로 방법론을 보완하고 이용 자료를 키워드와 논문초록으로 확대함으로써 구체적인 결과를 도출해야 할 것이다.

Keywords

References

  1. Kim Hyunjung et al., "Text mining-based emerging terend analysis for the aviation industry", Journal of intelligence and information systems, vol. 21, no. 1, 2015.
  2. Lim Siyeong et al., "Study on the trends of U-City and smart city researches using text mining technology", Journal of the korean society for geospatial information system, vol. 22, no. 3, 2014. DOI: https://doi.org/10.7319/kogsis.2014.22.3.087
  3. Kang Beomil et al., "A Study on opinion mining of newspaper texts based on topic modeling", Journal of the korean society for library and information science, vol. 47, no. 4, 2013. DOI: https://doi.org/10.4275/KSLIS.2013.47.4.315
  4. Kwon MiKyung, Analysis on the research trend of human resource development utilizing the keyword network analysis, Master's thesis, Sookmyung women's university, 2014.
  5. Kim Myungmi, Analysis of research trend on R&D performance using social network analysis, Master's thesis of the graduate school of Hanyang University, 2015.
  6. Oh Sungsam and Lee Munyoung, "An analytical study on korean agricultural education research trends", Journal of agricultural resource development, 15, 1990.
  7. Hong Seunggil et al., "Research trends in organic farming technology by journal article analysis", Korean journal of organic agriculture, vol. 22, no. 4, 2014.
  8. Park SungHee et al., "Analysis of the Agricultural Electronic Commerce: A Study on Documentary Research and Present Condition Comparison", Korean Journal on Food and Marketing Economics, vol. 31, no. 1, 2014.
  9. Korean Food Marketing Association, Journal of Food Marketing Economics, 1984-2015.
  10. Korea Rural Economic Institute, Journal of Rural Development, 1978-2015.
  11. Korean Agricultural Economics Association, Korean Journal of agricultural economics, 1958-2015.
  12. Korean Livestock Management Society and Korean Agri-Food Policy Society, Korean Journal of Agricultural Management and Policy, 2000-2015.
  13. Blei, M, A. Ng, M. Jordan, "Latent Dirichlet Allocations," Journal of Machine Learning Research, Vol. 3, 2003.
  14. Nahm ChoonHo, "An Illustrative Application of Topic Modeling Method to a Farmer's Diary", Journal of Comparative Culture Research, vol. 22, no. 1, 2016.
  15. Jin SeulA, "Topic-Network based Topic Shift Detection on Twitter", Korea Society for Information Management, vol. 20, no. 1, 2013. DOI: https://doi.org/10.3743/KOSIM.2013.30.1.285
  16. Korean Agricultural Economics Association, Agricultural Economics, Yulgok, 2014.
  17. Fumio Egaitu, Agricultural Economics, Youngbong You(Trans), Jeju University Press, 2007.
  18. Hiromi Tokoyama and Fumio Egaitu, Economics of Food System, Lee Byungoh and Ko Jongtae(Trans), Kangwon University Press, 2005.