The Journal of Korean Institute of Information Technology (한국정보기술학회논문지)

Korean Institute of Information Technology (한국정보기술학회)
권호 표지
DOI QR코드

DOI QR Code

Fashion Trend Marketing Prediction Analysis Based on Opinion Mining Applying SNS Text Contents

SNS 텍스트 콘텐츠를 활용한 오피니언마이닝 기반의 패션 트랜드 마케팅 예측 분석

  • Lee, Yoon-Ju (Dept. of Computer Science and Engineering, Incheon National University) ;
  • Seo, Ji-Hoon (Dept. of Computer Science and Engineering, Incheon National University) ;
  • Choi, Jin-Tak (Dept. of Computer Science and Engineering, Incheon National University)
  • 이윤주 (인천대학교 컴퓨터공학과) ;
  • 서지훈 (인천대학교 컴퓨터공학과) ;
  • 최진탁 (인천대학교 컴퓨터공학과)
  • Received : 2014.11.05
  • Accepted : 2014.12.06
  • Published : 2014.12.31
⟨ Previous Next ⟩

Abstract

Fashion trend has sensitive effects on marketing of the fashion industry in the global age that puts emphasis on diversity and personality of human. As a result, the IT industry actively researches technology of big data and wearable devices. Clothing is also magnified as the core technology of the next generation with graft of IT service, getting out of the recognition of simply wearing. Therefore, this paper aims at the emotion trend prediction analysis algorithm that can utilize fashion marketing by building the opinion dictionary of emotion based on related rules suitable for the Korea grammar through applying SNS text of the clients and analyzing fashion trend according to opinion information of the users as unformatted text. This proposal verified the accuracy of fashion trend analysis by applying the improved opinion dictionary of emotion that related rules of the Korean grammar are applied and collecting text associated with clothing from Twitter.

인간의 다양성과 개성을 중시하는 글로벌 시대에 있어서 패션계에서의 트랜드는 의류 산업 마케팅에 민감한 영향을 미치고 있다. 그로인해 IT 산업에서도 빅데이터와 웨어러블 디바이스 기술에 대한 연구가 활발하게 진행되고 있으며, 의류가 단순히 착용대상이라는 인식에서 벗어나 IT 서비스를 접목한 차세대 융합 기술의 핵심으로 부각되고 있다. 이에 따라 본 논문은 클라이언트의 SNS 텍스트를 활용하여 한국어 문법에 적합한 연관규칙 기반의 오피니언 감성사전을 구축하고, 비정형 텍스트인 사용자의 의견 정보에 따른 패션 트랜드를 분석하여 의류 마케팅에 활용할 수 있는 감성 트랜드 예측 분석 알고리즘을 제안하고자 한다. 본 제안은 트위터에서 의류 관련 텍스트를 수집하고, 한국어 문법의 연관 규칙을 적용한 향상된 오피니언 감성사전을 적용하여 패션 트랜드 분석의 정확도 향상을 검증하였다.

Keywords

Acknowledgement

Supported by : 국토교통부

References

  1. Chang-kyu Park, "Vision and Execution of the Fusion of IT and Textile Fashion: I-Fashion", Communications of the Korean Institute of Information Scientists and Engineers, Vol. 28, No. 7, pp. 70-74, July 2010.
  2. Hee-Sun Kim, "A Study on the Characteristics of Marketing Strategy on the SPA Brand", Journal of the Korean Fashion & Costume Design Association, Vol. 14, No. 1, pp. 131-150, Feb. 2012.
  3. Taejin Kim and Seongah Chin, "A Study on the State of Fashion Marketing Using the SNS -Focused on the Facebook-", Journal of the Korea Academia-Industrial Cooperation Society, Vol. 13, No. 5, pp. 2305-2312, 2012. https://doi.org/10.5762/KAIS.2012.13.5.2305
  4. Tae-Gyu Lee, Gi-Soo Chung, and Seong-Hoon Lee, "Fiber IT Convergence - Status of Digital Clothing and Wearable Computing", The Korean Institute of Information Technology Magazine, Vol. 10, No. 2, pp. 77-86, June 2012.
  5. Hae-Sung Lee and Joon-Hee Kwon, "A Survey on Users Emotional Model Based Mobile Music Streaming Service Using Wearable Sensors", Proceedings of KIIT Summer Conference, pp. 330-332, May 2010.
  6. Han-joon Kim and Jae-young Chang, "Discovering News Keyword Associations Using Association Rule Mining", The Journal of the Institute of Internet Broadcasting and Communication, Vol. 11, No. 6, pp. 63-71, Dec. 2011.
  7. Won-Jong Kim, Kang-Bok Lee and Myung-Gwan Kim, "An Implementation of Pan-So-Ri Classification Program Using Naive Bayesian Classifier", The Journal of the Institute of Internet Broadcasting and Communication, Vol. 11, No. 3, pp. 153-159, June 2011.
  8. Sung-Dong Kim, "Data Mining Tool for Stock Investors' Decision Support", Journal of the Korea Contents Association, Vol. 12, No. 2, pp. 472-482, Feb. 2012. https://doi.org/10.5392/JKCA.2012.12.02.472

Cited by

  1. 소비자 리뷰 텍스트마이닝을 이용한 신생 산업 시장 구조 분석: 국내 수제 맥주 시장의 경쟁 관계 및 시장 구조를 중심으로 vol.30, pp.2, 2014, https://doi.org/10.5859/kais.2021.30.2.189