Big Data Analysis of Food Tourism under COVID-19 Regulations in Jeonnam, Korea

Policy
By. Hyojin Kim Views. 1224

Abstract

Purpose: The purpose of this study is to collect and analyze big data related to food tourism in Jeollanam-do. Specifically, this study analyzes what potential tourists consider food tourism in Jeollanam-do under the COVID-19 regulation. It is to analyze the perception about Jeonnam as a destination for food tourism perceived by potential tourists through big data analysis. From big data analysis, this study analyzes why tourists visit Jeollanam-do for food tourism.

Method: The big data collected for analysis ranged from January 1 to December 31, 2021 using the Textom and came from the “N” firm, the largest portal site in Korea. Big data was collected from blogs, cafes, and news among various channels owned by the firm using the keyword ‘Jeonnam food tourism’. Two years have been since the first case of COVID-19 infection was reported in Korea on January 20, 2020. Word pairs were selected using the N-gram, which is a number that records the number of consecutive expressions of two words in the data. Key words were visualized using word cloud analysis.

Results: First, according to the frequency analysis through text mining, tourists who visit Jeollanam-do perceive Jeollanam-do as a food tourism destination. Second, in the frequency analysis using the N-gram, many words for tourism activities, tourism resources, tourism products, culture, and the like were presented in addition to food tourism in Jeollanam-do. Third, as in the frequency analysis using the N-gram and the result from word cloud, a specific region in Jeollanam-do such as Beopseongpo and a specific food such as kimchi were showed in the top search terms.

Conclusion: First, those officials who are charged in public relations and tourism in Jeollanam-do should prepare environments to provide unique tourism activities linked to food tourism and should develop various tourism programs in connection with food tourism in Jeollanam-do. Second, it is necessary to provide and promote fascinating programs that can be experienced from visit to Jeonnam, such as cultural tourism activities linked with food tourism and local tourism experiences. Finally, as the capital of food tourism, numerous advertisement and promotions for Jeonnam food and effective food-related festivals and events are necessary.

[Keywords] Big Data, Food Tourism, Covid-19, Regulations, Jeonnam



References

[1] Lee J & Liang D. Suggestions for using AI in Preparation for a Super-aging Society. Robotics & AI Ethics, 5(2), 57-64 (2020). [Article]
[2] Oh H. A Study on the Status and Recognition of Sports Welfare using Big Data Analysis. Kinesiology, 6(3), 22-33 (2021). [Article]
[3] Lee J. A Study on Anti-terror System using Big Data Based. International Journal of Criminal Study, 3(2), 18-22 (2018). [Article]
[4] Lee S. & Kim D. Policing against and Characteristic of Korean Organize Crime: Using Big Data. International Journal of Police and Policing, 3(1), 25-31 (2018). [Article]
[5] Park H. Police Activities in the Age of Big Data Utilization. International Journal of Protection, Security & Investigation, 3(1), 14-17 (2018). [Article]
[6] Bae S. & Kim D. A Semantic Network Analysis of Gastronomy Tourism using Social Big Data. Culinary Science & Hospitality Research, 27(8), 49-59 (2021).
[7] Dagvadorj A. & Kim H. A Study on Mongolian Food Awareness through Big Data Analysis: Using Google Search Resources. Culinary Science & Hospitality Research, 25(5), 34-42 (2019).
[8] Jung H. & Kim T. A Study on the Activation Plan of Namdo Bandatgil Culinary Tourism using Big Data Analysis. Culinary Science & Hospitality Research, 26(12), 129-140 (2020).
[9] Kim H. An Exploratory Study on the Semantic Network Analysis of Food Tourism through the Big Data. Culinary Science & Hospitality Research, 23(4), 22-32 (2017).
[10] Kwon H. & Kim H. Research Trend Analysis on Big Data through Topic Modeling and Semantic Network Analysis. Culinary Science & Hospitality Research, 27(3), 1-14 (2021).
[11] Ban H & Jun J. A Study on the Semantic Network Analysis of Luxury Hotel and Business Hotel through the Big Data. Culinary Science & Hospitality Research, 25(1), 18-28 (2019).
[12] Kim YJ & Kim HS & Kim HS. Understanding the Effects of Covid-19 on the Starbucks Perception through Big Data Analytics: A Comparative Study. Culinary Science & Hospitality Research, 27(6), 1-14 (2021).
[13] Dagvadorj A & Kim H & Kim W. A Study on the Future Direction of Food Tourism using Semantic Network Analysis: Focused on Coronavirus Infectious Disease-19 (Covid-19) Period. Culinary Science & Hospitality Research, 26(11), 272-282 (2020).
[14] Ban H & Kim H. Semantic Network Analysis of Hotel Package through the Big Data. Culinary Science & Hospitality Research, 25(2), 110-119 (2019).
[15] Ban H & Kim H. A Study on the Online Review Analysis of Restaurant Recognition in Busan: Especially Concerning Korean Reviews. Culinary Science & Hospitality Research, 35(2), 185-207 (2019).
[16] Jung E & Chang U. Tendency and Network Analysis of Diet Using Big Data. Journal Korean Dietetic Association, 22(4), 310-319 (2016).
[17] Kim H & Yim H. An Exploratory Study on the Semantic Network Analysis of Culinary Science & Hospitality Research through the Google Scholar. Culinary Science & Hospitality Research, 24(9), 1-10 (2018).
[18] Dagvadorj A & Ahn J & Kim H. A Study on Research Trend of Foodservice in Korean Schools using Semantic Network Analysis. Culinary Science & Hospitality Research, 27(3), 112-119 (2021).
[19] Lee S & Kim H. A Study on the Semantic Network Analysis of Cooking Academy through the Big Data. Culinary Science & Hospitality Research, 24(3), 167-176 (2018).
[20] Kim Y & Kim H. An Exploratory Study on the Conceptualization of Dessert Café through Big Data Analysis. Culinary Science & Hospitality Research, 25(5), 125-135 (2019).
[21] Kim H. A Semantic Network Analysis of Big Data regarding Food Exhibition at Convention Center. Culinary Science & Hospitality Research, 23(3), 257-270 (2017).
[22] Lee S & Jeon H. Influence of Big Data Based Majib Apps' Service Quality on Use Satisfaction and Reuse Intention of Majib Apps -Moderating Effect of Review Informativity-. Culinary Science & Hospitality Research, 22(5), 64-81 (2016).
[23] Jo A & Kim H. A Comparison of Starbucks between South Korea and U.S.A. through Big Data Analysis. Culinary Science & Hospitality Research, 23(8), 195-205 (2017).
[24] Lee S. A Study on Strategies for Busan Tourism Based on Big Data Analysis. Culinary Science & Hospitality Research, 26(1), 169-176 (2020).
[25] Kim Y & Ban H & Kim H. An Exploratory Study on the Semantic Network Analysis of Busan Tourism: Using Google Web and News. Culinary Science & Hospitality Research, 25(1), 126-134 (2019).
[26] Lee K & Scott N. Food Tourism Reviewed using the Paradigm Funnel Approach. Journal of Culinary Science & Technology, 13(2), 95-115 (2015).
[27] Rousta A. & Jamshidi D. Food Tourism Value: Investigating the Factors that Influence Tourists to Revisit. Journal of Vacation Marketing, 26(1), 73-95 (2020).
[28] Robinson RNS & Getz D. & Dolnicar S. Food Tourism Subsegments: A Data-driven Analysis. International Journal of Tourism Research, 20(3), 367-377 (2018).
[29] Tsai CTS & Wang YC. Experiential Value in Branding Food Tourism. Journal of Destination Marketing & Management, 6(1), 56-65 (2017).
[30] Rachão S & Breda Z & Fernandes C & Joukes V. Food Tourism and Regional Development: A Systematic Literature Review. European Journal of Tourism Research, 21(1), 33-49 (2019).
[31] Kim H & Hur J & Kang H. A Study on Stakeholder Awareness for the Revitalization of the Accommodation Industry in Gyeongbuk in the Era of Corona Coexistence: Focused on Focus Group Interview. Culinary Science & Hospitality Research, 28(1), 159-168 (2022).
[32] Hong I. Effect of Corona 19 Risk Perception on Eating Out Perception, Attitude, and Behavioral Intention. Culinary Science & Hospitality Research, 27(12), 174-182 (2021).
[33] Mun J & Ban H & Kim H. A Study on Consumers' Perception of Meal-kit: After the Spread of Coronavirus Infectious Disease-19 (Covid-19). Culinary Science & Hospitality Research, 26(10), 198-207 (2020).
[34] Choi S & Kim H & Jung O. An Analysis in Relationships between Comprehension and Satisfaction in the Class of the Analysis of Hospitality Firms after Covid-19. Culinary Science & Hospitality Research, 26(8), 227-234 (2020).
[35] Park S & Bae G. The Effect of Cleanliness Expectations on Consumer’s Menu Expectations and Purchase Intentions according to Hygiene Rating in Restaurant: Focusing on the Moderating Effects of Corona-19 Discomfort. Culinary Science & Hospitality Research, 26(12), 141-149 (2020).



Source: netongs.com