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KMID : 0881720230380050402
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Journal of Food Hygiene and Safety
2023 Volume.38 No. 5 p.402 ~ p.408
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Determining Food Nutrition Information Preference Through Big Data Log Analysis
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Song Ha-Na
Lee Hae-Jeung
Lee Hun-Joo
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Abstract
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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.
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KEYWORD
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Nutrient, Food Composition Database, Consumer preferences, Big data, Log analysis
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