Journal of the Korean Society of Clothing and Textiles (한국의류학회지)

The Korean Society of Clothing and Textiles (한국의류학회)
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A Study on the Evaluation of Fashion Design Based on Big Data Text Analysis -Focus on Semantic Network Analysis of Design Elements and Emotional Terms-

빅데이터 텍스트 분석을 기반으로 한 패션디자인 평가 연구 -디자인 속성과 감성 어휘의 의미연결망 분석을 중심으로-

  • An, Hyosun (Dept. of Fashion Industry, Ewha Womans University) ;
  • Park, Minjung (Dept. of Fashion Industry, Ewha Womans University)
  • 안효선 (이화여자대학교 의류산업학과) ;
  • 박민정 (이화여자대학교 의류산업학과)
  • Received : 2017.12.27
  • Accepted : 2018.04.13
  • Published : 2018.06.30
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Abstract

This study derives evaluation terms by analyzing the semantic relationship between design elements and sentiment terms in regards to fashion design. As for research methods, a total of 38,225 texts from Daum and Naver Blogs from November 2015 to October 2016 were collected to analyze the parts, frequency, centrality and semantic networks of the terms. As a result, design elements were derived in the form of a noun while fashion image and user's emotional responses were derived in the form of adjectives. The study selected 15 noun terms and 52 adjective terms as evaluation terms for men's striped shirts. The results of semantic network analysis also showed that the main contents of the users of men's striped shirts were derived as characteristics of expression, daily wear, formation, and function. In addition, design elements such as pattern, color, coordination, style, and fit were classified with evaluation results such as wide, bright, trendy, casual, and slim.

Keywords

References

  1. An, H., & Lee, I. (2015). A conceptual framework for Asian women's emotional needs in fashion design. International Journal of Fashion Design, Technology and Education, 8 (3), 206-213. doi:10.1080/17543266.2015.1053421
  2. An, H., & Lee, I. (2016a). Current status of Korean fashion design sensibility evaluation methods and their application overseas. Journal of the Korean Society of Clothing and Textiles, 40(4), 660-668. doi:10.5850/JKSCT.2016.40.4.660
  3. An, H., & Lee, I. (2016b). An investigation of a sensibility evaluation method using big data in the field of design- Focusing on Hanbok related design factors, sensibility responses, and evaluation terms-. Journal of the Korean Society of Clothing and Textiles, 40(6), 1034-1044. doi:10.5850/JKSCT.2016.40.6.1034
  4. An, H., & Park, M. (2017). A study on the user perception in fashion design through social media text-mining. Journal of the Korean Society of Clothing and Textiles, 41(6), 1060-1070. doi:10.5850/JKSCT.2017.41.6.1060
  5. Beheshti-Kashi, S., Lutjen, M., Stoever, L., & Thoben, K. D. (2015). TrendFashion-A framework for the identification of fashion trends. Proceedings of the INFORMATIK 2015, Germany, 1195-1205.
  6. Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243-269. doi:10.1016/S0378-8733(96)00301-2
  7. Gordon, A. (2008). Future savvy: Identifying trends to make better decisions, manage uncertainty, and profit from change. New York, NY: American Management Association.
  8. Kim, Y. H., & Kim, Y. J. (2016). Social network analysis (4th ed.). Seoul: Pakyoungsa.
  9. Kong, J. H., & Kwon, Y. A. (2013). Effect of stripe pattern for men's shirts on emotion. Korean Journal of The Science of Emotion & Sensibility, 16(1), 65-74.
  10. Kwon, O. K., Kim, H. E., & Na, Y. J. (2000). 패션과 감성과학 [Fashion and the science of sensibility]. Seoul: Kyomunsa.
  11. Lamb, J. M., & Kallal, M. J. (1992). A conceptual framework for apparel design. Clothing and Textiles Research Journal, 10(2), 42-47. doi:10.1177/0887302X9201000207
  12. Lee, S. S. (2013). 네트워크 분석 방법론 [Network analysis methods]. Seoul: Nonhyung.
  13. Narayanan, V. K., & Armstrong, D. J. (Eds.). (2005). Causal mapping for research in information technology. Hershey, PA: Idea Group Publishing.
  14. MISP. (2017, May 18). "Fashion MISP" 사용법 - [PRODUCT] 패션 빅데이터로 제품의 반응을 살펴본다! ["Fashion MISP" instructions - [PRODUCT] Explore product responses with fashion big data!]. NAVER 블로그-MISP Fashion [NAVER Blog-MISP Fashion]. Retrieved September 21, 2017, from https://blog.naver.com/fashionmisp/221008656641
  15. Rickman, T. A., & Cosenza, R. M. (2007). The changing digital dynamics of multichannel marketing: The feasibility of the weblog: Text mining approach for fast fashion trending. Journal of Fashion Marketing and Management: An International Journal, 11(4), 604-621. doi:10.1108/13612020710824634
  16. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
  17. Wollan, R., Smith, N., & Zhou, C. (2011). The social media management handbook: Everything you need to know to get social media working in your business. Hoboken, NJ: John Wiley & Sons, Inc.

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