• 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.

• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• Fashion & Textile Research Journal
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• v.23 no.2
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• pp.212-225
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• 2021

With the recognition of YouTube as an information search tool, YouTube creators have subsequently become sources of information to consumers. Accordingly, this study aims to analyze the consumers' response of famous fashion YouTubers in Korea, and to identify differences in consumer response based on the gender and generation of YouTubers. During the period from the opening of fashion creators' YouTube channels, we collected postings on blogs and Internet cafes using textom. As a result of preliminary investigation, six fashion YouTubers were selected. First, all the selected fashion YouTubers were well recognized by consumers as fashion informants. However, Milanonna has been shown to act as a life advisor and as an informant for luxury brands at the same time. Second, female fashion YouTubers were perceived with themes related to daily life, beauty, emotions, and mood rather than fashion itself; whereas, male fashion YouTubers appeared to be more interested in fashion accessories, especially with respect to the basic style. Third, Generation Z fashion YouTubers used the most non-fashion keywords, and their Millennial counterparts used keywords related to fashion items and product purchase properties. However, consumer response to OPAL fashion YouTubers have emerged with items such as life experiences, wisdom, and advice. Moreover, OPAL fashion YouTubers showed a variety of consumer assessments and the YouTuber's personal background. This study's analysis of the differences in the consumer response to fashion YouTubers based on gender and age enables the establishment of an appropriate strategy to attract target consumers and identify their appeal points.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.82-88
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• 2020

With the development of medical care in the 21st century and the rapid development of the 4th industry, electronic devices and household goods taking into account the physical and mental aging of the silver generation have been developed, and apps related to health and health are generally developed and operated. The apps currently used by the silver generation are a form that provides information on diseases by focusing on prevention rather than treatment, such as safety management apps for the elderly living alone and methods for preventing diseases. There are not many apps that provide information on foods that have a direct effect and nutrients in that food, and research on apps that can obtain information about individual foods is insufficient. In this paper, we propose an app that analyzes food factors and provides self-medication for health promotion of the silver generation. This app allows the silver generation to conveniently and easily obtain information such as nutrients, calories, and efficacy of food they need. In addition, this app collects/categorizes healthy food information through a textom solution-based crawling agent, and stores highly relevant words in a data resource. In addition, wide deep learning was applied to enable self-medication recommendations for food. When this technique is applied, the most appropriate healthy food is suggested to people with similar eating patterns and tastes in the same age group, and users can receive recommendations on customized healthy foods that they need before eating. This made it possible to obtain convenient healthy food information through a customized interface for the elderly through a smartphone.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.