[논문]빅데이터 텍스트 마이닝 분석을 활용한 아메카지 패션 트렌드 특징 고찰

김지형

doi:10.12940/jfb.2022.26.3.138

빅데이터 텍스트 마이닝 분석을 활용한 아메카지 패션 트렌드 특징 고찰
A Study on the Characteristics of Amekaji Fashion Trends Using Big Data Text Mining Analysis 원문보기

패션비즈니스 = Fashion business, v.26 no.3, 2022년, pp.138 - 154

김지형 (영산대학교 패션디자인학과)

Abstract ▼ AI-Helper

The purpose of this study is to identify the characteristics of domestic American casual fashion trends using big data text mining analysis. 108,524 posts and 2,038,999 extracted keywords from Naver and Daum related to American casual fashion in the past 5 years were collected and refined by the Textom program, and frequency analysis, word cloud, N-gram, centrality analysis, and CONCOR analysis were performed. The frequency analysis, 'vintage', 'style', 'daily look', 'coordination', 'workwear', 'men's wear' appeared as the main keywords. The main nationality of the representative brands was Japanese, followed by American, Korean, and others. As a result of the CONCOR analysis, four clusters were derived: "general American casual trend", "vintage taste", "direct sales mania", and "American styling". This study results showed that Japanese American casual clothes are influenced by American casual clothes, and American casual fashion in Korea, which has been reinterpreted, is completed with various coordination and creative styles such as workwear, street, military, classic, etc., focusing on items and brands. Looks were worn and shared on social networks, and the existence of an active consumer group and market potential to obtain genuine products, ranging from second-hand transactions for limited edition vintages to individual transactions were also confirmed. The significance of this study is that it presented the characteristics of American casual fashion trends academically based on online text data that the public actually uses because it has been spread by the public.

주제어

표/그림 (12)

그림 Figure 1. Search Results for the 'American Casual' Category in Musinsa Codimap (www.musinsa.com)
그림 Figure 2. Trend of Search Keywords about Amekaji (drawn by authors)
그림 Figure 3. Research Process (drawn by author)
표 Table 1. Collection Results of Keyword Related to Amekaji Fashion
그림 Figure 4.Word Cloud Results (taken by author)
표 Table 2. Results of Term Frequency Analysis (N=87)
표 Table 3. Results of Country of Origin of Amekaji Brands (N=87)
그림 Figure 5. Amekaji Fashion N-gram Network Analysis Results (taken by author)
표 Table 4. Results of Amekaji Fashion N-gram Network Analysis
표 Table 5. Results of the Centrality Analysis & TF-IDF of 40 Keywords
그림 Figure 6. Amekaji Fashion Network Visualization Analysis Results (taken by author)
그림 Figure 7. Amekaji Fashion CONCOR Visualization Analysis Results (taken by author)

참고문헌 (36)

Ahn, J. (2019, December 16). Instagram, the most followed hashtag in Korea this year is '#Gongstagram'. Global Economy Daily. Retrieved January 26, 2022, from www.getenews.co.kr
Amekaji look. (2019). Dictionary of current affairs. Retrieved January 4, 2022 from https://terms.naver.com/entry.naver?docId5764034&cid43667&categoryId43667
Analysis module. (2017). Textom. Retrieved January 2, 2022, from https://textom.co.kr/home/bbs/view.php?idnotice&page_num15&page1&no39
Bae, J. (2020). 4차 산업혁명과 스마트 비즈니스 [4th industrial revolution and smart business]. Seoul: Parkyoungsa.
Choi, Y., & Lee, K. (2021). Changes in consumer perception of one mile-wear and home wear: The impact of Covid-19 outbreak. Journal of Fashion Business, 25(2), 110-126. doi:10.12940/jfb.2021.25.2.110

원문보기 상세보기
Data, data everywhere. (2010, February 27). The Economist, pp. 4-6.
De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of big data based on its essential features. Library Review, 65(3), 122-135. doi:10.1108/LR-06-2015-0061

상세보기
Dijcks, J. (2011). Oracle: Big data for the enterprise. Oracle. Retrieved December 8, 2021, from https://www.oracle.com/technetwork/database/bi-datawarehousing/wp-big-data-with-oracle-521209.pdf
Fenn, J. (2010). Hype cycle for emerging technologies, 2010. Gartner. Retrieved January 2, 2022, from www.gartner.com
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. doi:10.1016/j.ijinfomgt.2014.10.007

상세보기
Gwak, H., & Lee, K. (2021). Consumer perception of types of fashion live commerce: Using text mining. Journal of Fashion Business, 25(3), 90-107. doi:10.12940/jfb.2021.25.3.90

원문보기 상세보기
Ha, J. (2002). Functionalism expressed in American fashion design. Journal of the Korean Society of Clothing and Textiles, 26(9/10), 1455-1466.
Heo, J. S., & Lee, E. J. (2019). Trend analysis of fashion brand evaluation using big data -Focusing on Gucci brand-. Journal of the Korean Society of Costume, 69(6), 38-51. doi:10.7233/jksc.2019.69.6.038

상세보기
Jeong, A. (2020). A study on the characteristics of Amekaji-look in Korea (Unpublished master's thesis). Seoul National University, Seoul, Korea.
Kim, C., & Ro, M. (2009). Diffusion and limitation of American trendy casual style in Korea -Focusing on the styles of American celebrities-. Journal of the Korean Society of Costume, 59(2), 128-142.
Kim, D. J., & Lee, S. (2019). A study of consumer perception on fashion show using big data analysis. Journal of Fashion Business, 23(3), 85-100. doi:10.12940/jfb.2019.23.3.85

원문보기 상세보기
Kim, H. (2001). A study on design features of unisex young casual wear. Journal of the Korean Society of Costume, 51(6), 85-99.
Kim, J., & Lee, J. (2018). Comparison and analysis of domestic and foreign sports brands using text mining and opinion mining analysis. The Journal of the Korea Contents Association, 18(6), 217-234. doi:10.5392/JKCA.2018.18.06.217

원문보기 상세보기
Kim, S. S., & Kim, Y. S. (2016). Study on recognitions of luxury brands by using social big data. Fashion & Textile Research Journal, 18(1), 1-14. doi:10.5805/SFTI.2016.18.1.1

원문보기 상세보기
Kim, Y. (2018). A study on Japanese men's street fashion -From Urahara to Neo-Urahara-. Journal of Fashion Design, 18(4), 1-16. doi:10.18652/2018.18.4.1

상세보기
Kim, Y. (2020). 소셜네트워크분석 기법의 이해와 적용: 네트워크 구조와 클러스터링 그리고 QAP [Understanding and application of social network analysis techniques: Network structure, clustering, and QAP]. Korea Insitute of Public Administration, 34, 58-68.
Koo, Y. S. (2020). Trend analysis on clothing care system of consumer from big data. Fashion & Textile Research Journal, 22(5), 639-649.

원문보기 상세보기
Korea Institute of S&T Evaluation and Planning. (2018). KISTEP Technology Trend Brief (2018-11). Retrieved December 8, 2021 from https://www.kistep.re.kr/flexer/view.jsp?FileDir/board/0031&SystemFileName1533627124484.pdf&ftypepdf&FileName1533627124484.pdf
Laney, D. (2001). 3-D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70).
Lee, K. (2008). Change of Japanese street fashion after the Second World War. Fashion & Textile Research Journal, 10(1), 30-39.
Lee, S. (2012). 네트워크 분석 방법론 [Network analysis method]. Seoul: Nonhyeong.
Marx, W. D. (2020). Ametora: How Japan saved American style. (S. Park, Trans.). Seoul: Workroom Press. (Original work published 2015).
Min, J. (2019, May 8). Come back! Biscuit pants...fashionable these days, wear work clothes. Hankyung Economy. Retrieved January 26, 2022, from www.hankyung.com
Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of big data. Somers, NY: IBM Institute for Business Value, Said Business School.
Search Results for the 'American Casual' Category in Musinsa Codimap. (n.d.). [Photograph]. Musinsa Codimap. Retrieved from https://store.musinsa.com/app/codimap/lists?style_typeamericancasual&tag_no&brand&display_cnt60&list_kindbig&sortdate&page1
Seon, J., Jung, H., & Lee, J. (2021). Changes in street fashion networks using social big data -Time-series approach to public attention and cluster attributes-. Journal of the Korean Society of Costume, 71(3), 124-142. doi:10.7233/jksc.2021.71.3.124

상세보기
Song, E., & Lim, H. (2021). Perceptions and trends of digital fashion technology -A big data analysis-. Fashion & Textile Research Journal, 23(3), 380-389. doi:10.5805/SFTI.2021.23.3.380

원문보기 상세보기
Sung, K. S. (2020). Research on the reaction to Newtro fashion through social media. The Treatise on The Plastic Media, 23(2), 10-18. doi:10.35280/KOTPM.2020.23.2.2

상세보기
Yadranjiaghdam, B., Pool, N., & Tabrizi, N. (2016). A survey on real-time big data analytics: Applications and tools. 2016 International Conference on Computational Science and Computational Intelligence (pp.404-409). Las Vegas, NV: IEEE
Yum, H. (1999). A study on street fashion in Japan - Focusing on the 1980s-. Journal of Fashion Business, 3(4), 55-66.
Yum, H. (2004). A study on the Japanese street fashion since the 1990's. Journal of Fashion Business, 8(2), 102-115.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증