• 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.

• Korean Journal of Childcare and Education
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• v.20 no.1
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• pp.91-111
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• 2024

Objective: The purpose of this study is to explore trends in institutional evaluation research in early childhood education through keyword network analysis. This aims to understand trends in academic discourse on institutional evaluation and gain implications for follow-up research and related policy directions. Methods: A total of 6,629 keywords were extracted from 572 dissertations and journal articles published from January 2006 to October 2023 for the purpose of analyzing and visualizing the frequency and centrality of keywords, as well as the structural properties of keyword networks. The analysis and visualization were conducted using the TEXTOM, UCINET6, and NetDraw programs. Results: First, the number of institutional evaluation studies increased steadily from 2006 to 2010 and then decreased, with a higher frequency of studies on daycare centers compared to kindergartens. Second, the most frequently occurring keyword in the analysis was 'daycare center,' and the highest connection strength was found in the term 'daycare-center-evaluation.' Third, network analysis revealed that key terms for institutional evaluation research included 'evaluation certification,' 'recognition,' 'evaluation indicators,' 'teacher,' 'daycare center,' and 'kindergarten.' In the ego network analysis for each institution, 'parent' emerged as a highly ranked keyword. Conclusion/Implications: This study confirmed the perspectives of previous studies by revealing the structure of core concepts in early childhood education institution evaluation research, and provided implications for follow-up and direction of institution evaluation

• Journal of Korean society of Dental Hygiene
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• v.23 no.6
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• pp.451-458
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• 2023

Objectives: The purpose of this study is to explore the research trends of elderly oral health through a language network analysis. Methods: A total of 354 published studies with 668 keywords were collected from the Research Information Sharing Service (RISS) between 2000 and 2022. Language network analysis was performed using Textom 6.0, Ucinet 6.774, and NetDraw 2.183. Results: The most frequent keywords were 'elderly', 'oral health', 'quality of life', and 'OHIP-14'. The result of frequency-inverse document frequent keywords showed similar results to the most frequent keywords. The N-gram of keywords shows that 'elderly', 'oral health' (18 times) and 'elderly', 'depression' (7 times). As a results of the analysis of degree centrality and between centrality, 'elderly', 'oral health', and 'quality of life' were found to be high. The CONCOR analysis identified the main clusters of 'quality of life', 'oral health behavior', 'health', and 'oral function disorder'. Conclusions: The results of the current study could be available to know research trends in elderly oral health and it is necessary to improve more comprehensive study in follow-up study.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.270-275
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• 2024

Introduction. In this study, purpose is to analize the types of golf tourism, inbound or outbound, by using big data and see how movement of industry is being changed and what changes have been made during and after Covid-19 in golf industry. Method Using Textom, a big data analysis tool, "golf tourism" and "Covid-19" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 1 st January, 2023 to 31st December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "golf tourism" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, top 36 keywords with the highest relevance and search frequency were selected and applied to this study. The top 36 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. Results By using big data analysis, it was found out option of oversea golf tourism is affecting on inbound golf travel. "Golf", "Tourism", "Vietnam", "Thailand" showed high frequencies, which proves that oversea golf tour is now the re-coming trends.

• The Research Journal of the Costume Culture
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• v.28 no.2
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• pp.265-284
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• 2020

This study aimed to analyze the performance of Disney-collaborated fashion lines based on online consumer reviews. To do so, the researchers employed text mining and network analysis to identify key words in the reviews of these products. Blogs, internet cafes, and web documents provided by Naver, Daum, and YoutTube were selected as subjects for the analysis. The analysis period was limited to one year after for the 2019. Data collection and analysis were conducted using Python 3.7, Textom, and NodeXL. The research terms in question were as follows: 'Disney fashion collaboration' and 'Frozen fashion collaboration'. Preliminary survey results indicated that 'Elsa's dress' was the most frequently mentioned term and that the domestic fashion brand Eland Retail was the most active in selling Disney branded clothing through its own brand. The writers of reviews for Disney-collaborated fashion products were primarily mothers with daughters. Their decision to purchase these products was based upon the following factors; price, size, stability of decoration, shipping, laundry, and retailer. The motives for purchasing the product were the positive response of the consumer's child and the satisfaction of the parents due to the child's response. The problems to be solved included insufficient quantity of supply, delay in delivery, expensive price considering the number of times children's clothes are worn, poor glitter decoration, faded color, contamination from laundry, and undesirable smells immediately after the purchase.

• The Research Journal of the Costume Culture
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• v.28 no.4
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• pp.420-428
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• 2020

This study analyzed the expression of hanbok in online fashion styling games, and examined the main themes of and user reactions to hanbok fashion styling games through semantic network analysis and sentiment analysis. KrKwic, Textom, and NodeXL were used for data collection and analysis. The findings of the study are as follows: First, depending on the designer's expressive method, hanbok fashion styling games provide typical traditional hanbok, modern hanbok, and dress-like fusion hanbok, demonstrating contents with various themes for different sexes, classes, and situations. Second, as a result of analyzing the themes of hanbok-related styling game contents, it turned out that the main themes are tradition, color, historical drama, fusion hanbok, holiday, and love. Most of them produce modernized hanbok that reflect the situation and utility rather than strictly traditional ones. Third, as a result of analyzing user reviews of hanbok styling game contents, positive factors mostly turned out to be the satisfaction of the hanbok contents, with the users also showing positive intent to wear hanbok. Through this, it was found out that hanboks portrayed in game contents could potentially cause the user's positive intent to wear hanbok. Negative factors turned out to be the discordance between the intrinsic image of hanbok and the game character's image.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.265-274
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• 2017

This paper examines the problems of big data analysis education and suggests ways to solve them. Big data is a trend that the characteristic of big data is evolving from V3 to V5. For this reason, big data analysis education must take V5 into account. Because increased uncertainty can increase the risk of data analysis, internal and external structured/semi-structured data as well as disturbance factors should be analyzed to improve the reliability of the data. And when using opinion mining, error that is easy to perceive is variability and veracity. The veracity of the data can be increased when data analysis is performed against uncertain situations created by various variables and options. It is the node analysis of the textom(텍스톰) and NodeXL that students and researchers mainly use in the analysis of the association network. Social network analysis should be able to get meaningful results and predict future by analyzing the current situation based on dark data gained.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• Journal of Fashion Business
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• v.23 no.5
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• pp.81-95
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• 2019

The purpose of this study is to research consumers' perception of the HiSeoul fashion show, which is being used by new designers as a means of promotion, and to propose a strategy for revitalizing new designer brands. This was done in order to secure basic data from fashion consumers, to help guide marketing strategies and promote rising designers. In this research, the consumers' perception of HiSeoul fashion show was verified using text-mining, data refinement and word clouding that was undertaken by TEXTOM3.0. Also, semantic network analysis, CONCOR analysis and visualization of the analysis results were performed using Ucinet 6.0 and NetDraw. "HiSeoul fashion show" was used as the keyword for text-mining and data was collected from March 1, 2018 to April 30, 2019. Using frequency analysis, TF-IDF, and N-gram, it was also shown that consumers are aware of places where shows are held, such as DDP and Igansumun. It was also revealed that consumers recognize rising designer brands, designer's names, the names of guests attending the show and the photo times. This study is meaningful in that it not only confirmed consumers' interest in new designer brands participating in the HiSeoul Fashion Show through big data but also confirmed that it is available as a marketing strategy to boost brand sales. This study suggests using HiSeoul show room to induce consumer sales, or inviting guests that match the brand image to promote them on SNS on the day the show is held for a marketing strategy.

• Fashion & Textile Research Journal
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• v.21 no.3
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• pp.277-287
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• 2019

This study analyzes the online issues of design innovation by a fashion creative director. The study selected fashion house Gucci as the main subject and analyzed social media posts. As for study methods, a social matrix program Textom 2.0 collected 13,014 nouns and adjectives using 'Gucci Collection' as a search keyword from Naver Blogs from March to August 2014 and from March to August 2016. Design related issues were derived through semantic network analysis using Ucinet6 and the NetDraw program. The results of the keyword frequency analysis showed that social media user interest for the Gucci collection increased based on the rapid increase in the number of posts from 1,064 to 2,126 after changing the fashion creative director. The results of visualization of semantic network analysis and content analysis also showed that the main issues related to the Gucci collection design changed after the replacement of the fashion creative director. The study found that issues formed around the product information worn by celebrities for promotion purposes during the 2014 period; however, during the 2016 period, issues were formed around 'vintage' and 'retro' runway concepts with design styles related to Alessandro Michele, the new creative director.