• Journal of Korean Home Economics Education Association
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• v.36 no.2
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• pp.1-13
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• 2024

This study used TEXTOM for a total of 4 years, 2 years before and after, as of January 19, 2020, when the domestic confirmed cases of COVID-19 were officially announced, targeting Naver, Daum, Google, YouTube, and Twitter. By analyzing changes in adolescents' interest and awareness of their dietary habits, we aimed to create an opportunity to develop a dietary education program to provide proper dietary education. The results obtained through this study are as follows. First, the keywords with the highest co-occurrence before COVID-19 were 'nutrition' and 'counseling', and the next keywords were 'nutrition' and 'education'. After COVID-19, the order was 'nutrition', 'education', 'food' and 'safety'. Second, the results of co-occurrence frequency network analysis showed that there was high interest in nutrition and counseling regardless of COVID-19, and that interest in safety and health increased further after COVID-19. Third, through cluster formation through CONCOR analysis, before COVID-19, it was categorized into 'diet and physical activity', 'skin and disease', 'health and food', and 'nutrition and intake', and after COVID-19, it was categorized into 'nutrition, intake and COVID-19', 'diet and physical activity', 'skin and disease', and 'circadian rhythm imbalance and disease'. Fourth, as a result of the diet-related keyword cluster analysis network, before COVID-19, keywords in the 'eating and physical activity' group were strongly connected to keywords in the 'health and food' and 'nutrition and intake' groups, and after COVID-19, 'diet' Keywords in the 'and physical activity' group were strongly connected to keywords in the 'nutrition, intake, and COVID-19' group.

• Culinary science and hospitality research
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• v.24 no.2
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• pp.34-43
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• 2018

The objective of this study was to acquire a general and text-based awareness and recognition of cruise food hygiene through big data analytics. For the purpose, this study collected data with conducting the keyword "food hygiene, cruise" on the web pages and news on Google, during October 1st, 2015 to October 1st, 2017 (two years). The data collection was processed by SCTM which is a data collecting and processing program and eventually, 899 kb, approximately 20,000 words were collected. For the data analysis, UCINET 6.0 packaged with visualization tool-Netdraw was utilized. As a result of the data analysis, the words such as jobs, news, showed the high frequency while the results of centrality (Freeman's degree centrality and Eigenvector centrality) and proximity indicated the distinct rank with the frequency. Meanwhile, as for the result of CONCOR analysis, 4 segmentations were created as "food hygiene group", "person group", "location related group" and "brand group". The diagnosis of this study for the food hygiene in cruise industry through big data is expected to provide instrumental implications both for academia research and empirical application.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.151-158
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• 2019

This study aimed to analyze the research trend related on obesity in physical education in Korea through the keyword network analysis and to establish a basic database for effective design of prospective studies. To achieve it the study crawled co-occured keywords with 'obesity' and 'physical education' from RISS and analyzed the list from 1990 to 2018. They include 25 journal papers and 38 dissertations. The results are as follows. First, recent 30 years 63 papers published in Korea with 'Obesity' and 'Physical Education', and there were 144 related keywords. Second, analyzing journals which have 'Obesity' and 'Physical Education', co-occured keywords in 4 centrality were 24 keywords(student, Korea, prevention, effect, level, body, activation, actual condition, lesson, child, investigation, participation, book, cause, activity, normal, degree, nutrition, physical strength, weight, elementary, light, inquiry, health), and 37 keyword occurred in top 30. Lastly, by CONCOR analysis the result could be divided into 2 clusters. One consists of the object of obesity and its invervention, and the other consists of negative keywords of obesity and its preliminery dimenstion. Through the result, this study showed the research trend which involves the concept of obesity in physical education in Korea. Through the result, prospective obesity research in physical education in Korea would be promoted.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.72-78
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• 2021

Currently, the Korean wave is not limited to popular culture, but has a significant impact not only on Korea's national image but also on the improvement of Korean companies' products and image of Korea. In this study, using Textom to confirm the change in foreign tourists' image of Korea, the data collection period was 1 year of 2020, when COVID 19 occurred, as a collection period for "Korea and foreigner" and related key words, each Hallyu content, and ranked in the top 80 keywords were derived. Centrality analysis for semantic network visualization was performed using UCINET6, and through CONCOR analysis, 7 groups 'K-Quarantine ', 'K-Drama', 'K-Movie', 'K-Beauty', 'K-Shopping', It was clustered into 'K-Tech' and 'K-Pop'. As a result of the analysis, the image of Korea abroad generally recognized the Korean Wave as cultural content, but after the outbreak of COVID 19, it is judged that it has been recognized as a country with a successful case of K-Quarantine.

• The Research Journal of the Costume Culture
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• v.29 no.4
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• pp.603-615
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• 2021

The aim of this study was to analyze the semantic network structure of keywords and the visual composition of images extracted from Instagram in relation to the multi-persona phenomenon with in fashion imagery, which has recently been attracting attention. To this end, the concept of a 'secondary character', which forms a separate identity from a 'main character' on various social media platforms as well as on the airwaves, was considered as the spread of multi-persona and #SecondaryCharacter on Instagram was investigated. 3,801 keywords were collected after crawling the data using Python and morphological analysis was undertaken using KoNLP. The semantic network structure was then examined by conducting a CONCOR analysis using UCINET and Netdraw to determine the top 50 keywords. The results were then classified into a total of 6 clusters. In accordance with the meaning and context of the keywords included in each cluster, group names were assigned : virtual characters, relationship with the main character, hobbies, daily record, N-job person, media and marketing. Image analysis considered the technical, compositional, and social styles of the media based on Gillian Rose's visual analysis method. The results determined that Instagram uses fashion images that virtualize one's face to produce multi-persona representation s that show various occupations, describe different types of hobbies, and depict situations pertaining to various social roles.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.206-211
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• 2022

As the level of consumption is improved and cultural life is pursued, the consumer's consciousness structure is rapidly changing, and the demand for product selection level, variety, and quality is becoming more diverse. The restaurant economy is falling due to the prolonged COVID-19, the economic recession, income decline, and changes in population structure and lifestyle, but the Meal- kit market is growing rapidly. This study aims to identify the consumer perception of Meal-kit, which is rapidly growing as an alternative to existing meals in the fields of dining out, food, and distribution due to the development of technology and social environment using big data. As a result of the analysis, the keywords with the highest frequency of appearance were in the order of Meal-kit, Cooking, Product, Launching, and Market and were divided into 8 groups through the CONCOR analysis. We want to identify consumer trends related to the key keywords of Meal-kit, present effective data related to Meal-kit demand for Meal-kit specialized companies, and provide implications for establishing marketing strategies for differentiated competitive advantage.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.804-821
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• 2023

This study examines current trends and perceptions of veganism and vegan fashion in Korea, China, and the United States. Using big data tools Textom and Ucinet, we conducted cluster analysis between keywords. Further, frequency analysis using keyword extraction and CONCOR analysis obtained the following results. First, the nations' perceptions of veganism and vegan fashion differ significantly. Korea and the United States generally share a similar understanding of vegan fashion. Second, the industrial structures, such as products and businesses, impacted how Korea perceived veganism. Third, owing to its ongoing sociopolitical tensions, the United States views veganism as an ethical consumption method that ties into activism. In contrast, China views veganism as a healthy diet rather than a lifestyle and associates it with Buddhist vegetarianism. This perception is because of their religious history and culinary culture. Fundamentally, this study is meaningful for using big data to extract keywords related to vegan fashion in Korea, China, and the United States. This study deepens our understanding of vegan fashion by comparing perceptions across nations.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.87-96
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• 2023

ChatGPT, a leader in generative AI, can use natural expressions like humans based on large-scale language models (LLM). The ability to grasp the context of the language and provide more specific answers by algorithms is excellent. It also has high-quality conversation capabilities that have significantly developed from past Chatbot services to the level of human conversation. In addition, it is expected to change the operation method of the tourism industry and improve the service by utilizing ChatGPT, a generative AI in the tourism sector. This study was conducted to explore ChatGPT trends and paradigms in tourism. The results of the study are as follows. First, keywords such as tourism, utilization, creation, technology, service, travel, holding, education, development, news, digital, future, and chatbot were widespread. Second, unlike other keywords, service, education, and Mokpo City data confirmed the results of a high degree of centrality. Third, due to CONCOR analysis, eight keyword clusters highly relevant to ChatGPT in the tourism sector emerged.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.1-19
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• 2023

This study examines Korean users' attitudes and emotions toward Melon and Spotify, which lead the music streaming market. We used Text Mining, Semantic Network Analysis, TF-IDF, Centrality, CONCOR, and Word2Vec analysis. As a result of the study, MelOn was used in a user's daily life. Based on Melon's advantages of providing various contents, the advantage is judged to have considerable competitiveness beyond the limits of the streaming app. However, the MelOn users had negative emotions such as anger, repulsion, and pressure. On the contrary, in the case of Spotify, users were highly interested in the music content. In particular, interest in foreign music was high, and users were also interested in stock investment. In addition, positive emotions such as interest and pleasure were higher than MelOn users, which could be interpreted as providing attractive services to Korean users. While previous studies have mainly focused on technical or personal factors, this study focuses on consumer reactions (online reviews) according to corporate strategies, and this point is the differentiation from others.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.108-113
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• 2024

The purpose of this study is to analyze user perceptions of tourism platforms through big data. Data were collected from Naver, Daum, and Google as big data analysis channels. Using semantic network analysis with the keyword 'tourism platform,' a total of 29,265 words were collected. The collection period was set for two years, from August 31, 2021, to August 31, 2023. Keywords were analyzed for connected networks using TexTom and Ucinet programs for social network analysis. Keywords perceived by tourism platform users include 'travel,' 'diverse,' 'online,' 'service,' 'tourists,' 'reservation,' 'provision,' and 'region.' CONCOR analysis revealed four groups: 'platform information,' 'tourism information and products,' 'activation strategies for tourism platforms,' and 'tourism destination market.' This study aims to expand and activate services that meet the needs and preferences of users in the tourism field, as well as platforms tailored to the changing market, based on user perception, current status, and trend data on tourism platforms.