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빅데이터 로그분석을 통한 식품영양정보 선호도 분석

Determining Food Nutrition Information Preference Through Big Data Log Analysis

  • 송하나 (켐아이넷(주) 식의약융합연구팀 ) ;
  • 이해정 (가천대학교 식품영양학과) ;
  • 이헌주 (켐아이넷(주) 식의약융합연구팀 )
  • Hana Song (Department of Food and Drug Informatics Research, CHEM. I. NET, Ltd.) ;
  • Hae-Jeung, Lee (Department of Food and Nutrition, Gachon University) ;
  • Hunjoo Lee (Department of Food and Drug Informatics Research, CHEM. I. NET, Ltd.)
  • 투고 : 2023.10.11
  • 심사 : 2023.10.18
  • 발행 : 2023.10.30

초록

국내 소비자들의 식품 영양성분에 대한 관심이 계속적으로 증가하고 있지만 영양성분과 관련된 식품의 소비자 선호도 분석 연구는 부족한 실정이다. 본 연구는 대국민 정보 서비스인 식품영양성분 데이터베이스 플랫폼에 수집된 빅데이터의 로그분석을 수행하여 소비자들이 영양학적 측면에서 관심을 가지는 식품에 대한 선호도 결과를 제시하였다. 수집 기간은 2020년 1월부터 2022년 12월까지의 3개년으로 설정하여 총 2,243,168건의 식품명 검색어가 수집되었으며, 식품명을 병합하여 품목대표 식품명으로 가공하였다. 분석도구는 R프로그램을 이용하였으며, 영양정보를 확인하고자 하는 식품명의 검색 빈도를 전체 기간 및 계절별로 분석하였다. 전체 기간 동안 빈도수 분석 결과, 한국인이 일반적으로 자주 섭취하는 쌀밥, 닭고기, 달걀의 빈도수가 가장 높았다. 계절성에 따른 선호도 분석 결과, 봄과 여름에는 대체적으로 국물이 없고 뜨겁지 않은 음식의 빈도수가 높았으며, 가을과 겨울에는 국물이 있고 따뜻한 음식의 빈도수가 높았다. 또한, 외식업체에서 계절식품으로 판매하는 냉면, 콩국수 등과 같은 식품의 빈도수도 계절성을 가지는 것으로 확인되었다. 이러한 결과는 소비자들이 일반적으로 자주 섭취하는 식품의 영양정보에 관심을 가지는 패턴을 확인할 수 있었으며, 소비 트렌드와 간접적인 연관성을 가진다는 점에서 외식업계에서 계절별 마케팅 전략 수립 시 기초 자료로 활용될 수 있을 것으로 기대된다.

Consumer interest in food nutrition continues to grow; however, research on consumer preferences related to nutrition remains limited. In this study, big data analysis was conducted using keyword logs collected from the national information service, the Korean Food Composition Database (K-FCDB), to determine consumer preferences for foods of nutritional interest. The data collection period was set from January 2020 to December 2022, covering a total of 2,243,168 food name keywords searched by K-FCDB users. Food names were processed by merging them into representative food names. The search frequency of food names was analyzed for the entire period and by season using R. In the frequency analysis for the entire period, steamed rice, chicken, and egg were found to be the most frequently consumed foods by Koreans. Seasonal preference analysis revealed that in the spring and summer, foods without broth and cold dishes were consumed frequently, whereas in fall and winter, foods with broth and warm dishes were more popular. Additionally, foods sold by restaurants as seasonal items, such as Naengmyeon and Kongguksu, also exhibited seasonal variations in frequency. These results provide insights into consumer interest patterns in the nutritional information of commonly consumed foods and are expected to serve as fundamental data for formulating seasonal marketing strategies in the restaurant industry, given their indirect relevance to consumer trends.

키워드

과제정보

본 연구는 2023년도 식품의약품안전처의 연구개발비(23192영양안061)의 지원에 의해 이루어졌으며, 이에 감사드립니다.

참고문헌

  1. Lee, G.T., A study on the consumer perceptions of meal-kits using big data. Int. J. Tour. Hosp. Res., 35, 227-239 (2021). 
  2. Choi, H.Y., Kim, D.J., Network analysis of consumer perception using social big data: focused on foodtech. Culi. Sci. Hos Res., 28, 70-82 (2022). 
  3. Jang, H.H., Woo, S.Y., Heo, S.E., Jeong, S.Y., Kim, J.R, Kwon, K.S, Chun, H.S., Korean consumers' perceptions of food that was intentionally falsified the country of origin using social big data. Safe Food, 12, 68-74 (2017). 
  4. Yoo, J.A., Jeong, H.S., Consumer awareness of nutrition labelling in restaurants according to level of health consciousness. Korean J. Food Nutr., 24, 282-290 (2011).  https://doi.org/10.9799/ksfan.2011.24.3.282
  5. Lee, H.S., Chang, M.J., Kim, H.Y., Shim, J.S., Lee, J.S., Kim, K.N., Survey on utilization and demand for national food composition database. J. Nutr. Health, 51, 186-198 (2018).  https://doi.org/10.4163/jnh.2018.51.2.186
  6. Ministry of Food and Drug Safety (MFDS), (2023, August 23). Korean food composition database system. Retrieved from https://various.foodsafetykorea.go.kr/nutrient/. 
  7. U.S. Department of Agriculture (USDA), Fooddata central. (2023). Retrieved from https://fdc.nal.usda.gov/data-documentation.html. 
  8. European Food Information Resource (EuroFIR). Food data (2023). Retrieved from https://www.eurofir.org/food-information. 
  9. Koh, J., Chong, Y., Analysis of Taiwanese food trends in Korea using social big data: focusing on blog text analysis. TMRO, 24, 71-91 (2020).  https://doi.org/10.18604/tmro.2020.24.3.4
  10. Lim, C.I., Lee, J.S., Choi, J.W., Choi, J.S., Analysis of buying behavior and preference to fruits in Korea. Korean J. Hortic. Sci. Technol., 23, 351-355 (2005). 
  11. Yi, N.Y., Choi, J.H., A study of the consumer perception of meal kit using big data analysis. Food Serv. Ind. J, 15, 211-222 (2019). 
  12. Ahn, S., Lee, K., Lee, J., Kim, E., A semantic network analysis of parenting stress on social media. JKHMA, 38, 61-77 (2020).  https://doi.org/10.7466/JKHMA.2020.38.1.61
  13. Lee, E.J., Jung, H., Jang, J., A study on diet perceptions and trends before and after COVID-19 using big data analysis. J. Korean Soc. Food Sci. Nutr., 52, 659-671(2023).  https://doi.org/10.3746/jkfn.2023.52.6.659
  14. Kim, Y.M., Kim, S.C., A study on the changes in eating out consumption using big data after COVID-19. J. Prod. Res., 40, 91-101 (2022). 
  15. Park, H.W., Nah, K., Study on restaurant franchise service reflected on a single household consumer patterns: Focus on 2030 generation. J. Digit. Des., 15, 697-704. 
  16. Kwan, K.I., Yoon, S.W., Kim, S.J., Kang, H., Kim, H.N., Kim, J.Y., Kim, S.Y., Kim, K., Lee, J.H., Jung, S.M., Ock, S.W., Lee, E.J., Kim, J.W., Kim, M.C., Park, H.K., A survey on customers' perceptions of nutrition labeling for processed food and restaurant meal. Korean J. Nutr., 43, 181-188 (2010).  https://doi.org/10.4163/kjn.2010.43.2.181
  17. Lee, Y.J., Kim, G.J., Exploratory study of post-COVID-19 changes in eating behaviors: Focused on behavior of restaurant visit, home eating behavior and delivery food purchase behavior. Culi. Sci. Hos. Res., 27, 133-142 (2021). 
  18. Costa, A.I.A., Dekker, M., Beumer, R.R., Rombouts, F.M., Jongen, W.M.F., A consumer-oriented classification system for home meal replacements. Food Qual. Prefer., 12, 229-242 (2001).  https://doi.org/10.1016/S0950-3293(01)00010-6
  19. Barreiro-Hurle, J., Gracia, A., de-Magistris, T., The effects of multiple health and nutrition labels on consumer food choices. J. Agric. Econ., 61, 426-443 (2010).  https://doi.org/10.1111/j.1477-9552.2010.00247.x
  20. Wagner, C., Singer, P., Strohmaier, M., The nature and evolution of online food preferences. EPJ Data Sci., 3, e38 (2014).