• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.165-170
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• 2024

'K-contents' is in the spotlight worldwide. As the prefix 'K' became popular, interest in K-food(Korean food) also increased. Various studies on K-contents are being conducted, but research on K-food is still very limited. References and articles about K-food are mainly limited to the overseas expansion, marketing status, and sales of domestic brands, and a few research papers deal with only cases of a specific brand's overseas expansion. This paper aims to analyze how K-food is represented in TV unscripted shows and TV series produced in Korea and what their effects are through empirical works. Among the unscripted shows based on food, they are estimated that the point of competitiveness as K contents deal with foreigners' Korean food experiences. Representative examples here are the way foreigners who visit Korea experience Korean food as part of their Korean culture experience, or the type of temporarily setting up a restaurant overseas to sell Korean food to local people. However, the problem with such shows are that it lacks long-term appeal because it is based on the 'Gukbbong(a slang term for 'extreme nationalism')' sentiment. The exposure of K-food in K-contents creates a tremendous advertising effect. It is judged that the current status and analysis of K-contents based on K-food can help establish the direction of future program production and the identity of K-food.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.189-198
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• 2019

This study aims to make meaning of the reactions of the Korean people through the text mining of comments on videos of the December 2018 MMA performance of intro on YouTube. For this, comments on 15 YouTube videos were collected over the past 10 months. With the collected data, a total of 5,135 comments were analyzed through crawling using the Python and BeautifulSoup programs, data was refined over a total of 3 sessions, and a final total of 5,080 comments were used as analysis material. A mining technique was used for data analysis and the process of refinement, analysis, and visualization was achieved using the Textom program. Research results showed that keyword analysis showed the keywords of 'performance', 'Korea', 'video', 'top', 'cool', 'dance', 'idol', 'legend', 'love', and 'gratitude' in that order and keywords such as 'patriotism' and 'Olympics' also appeared frequently. N-gram analysis showed that comments with contexts such as 'a top performance that will remain a legend among Korean idol performances', and 'an idol performance that displayed the traditional culture of Korea' were in higher ranks. Based on such keyword analysis results, topic modeling was applied and 5 top keywords were extracted from a total of 5 topics. Analysis results of topic contents and distribution showed that topics in the comments of this performance's videos largely consisted of the 3 reactions of 'high praise regarding the stage performance', 'affection towards the fusion and artistic sublimation of Korean traditional dance', and 'gratitude towards the uploading of cool dance videos'