빅데이터를 통한 2016년의 다이어트 실태 분석

• Published in: Han'guk Sikp'um Kwahakhoe Chi = Korean Journal of Food Science and Technology Vol. 51; no. 2; pp. 176 - 181
• Main Authors: 정은진(Eun-Jin Jung), 장은재(Un-Jae Chang), 조경애(Kyungae Jo)
• Format: Journal Article
• Language: Korean
• Published: Seoul 한국식품과학회 01.04.2019; Korean Society of Food Science & Technology
• Subjects: Big Data; Carbohydrates; Data management; Diet; Frequency analysis; High carbohydrate diet; High fat diet; Low carbohydrate diet; Low fat diet; Mass media; Monitoring; Seasonal variations; 식품과학; continuous monitoring; exercise; big data; commercial diet; diet menu
• ISSN: 0367-6293; 2383-9635
• DOI: 10.9721/KJFST.2019.51.2.176

The aim of this study was to analyze dieting practices and tendencies in 2016 using big data. The keywords related to diet were collected from the portal site Naver and analyzed through simple frequency, N-gram, keyword network, and analysis of seasonality. The results showed that exercise had the highest frequency in simple frequency analysis. However, diet menu appeared most frequently in N-gram analysis. In addition, analysis of seasonality showed that the interest of subjects in diet increased steadily from February to July and peaked in October 2016. The monthly frequency of the keyword highfat diet was highest in October, because that showed the 'Low Carbohydrate High Fat' TV program. Although diet showed a certain pattern on a yearly basis, the emergence of new trendy diets in mass media also affects the pattern of diet. Therefore, it is considered that continuous monitoring and analysis of diet is needed rather than periodic monitoring.