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http://dx.doi.org/10.5850/JKSCT.2022.46.6.1142

Development of Online Fashion Thesaurus and Taxonomy for Text Mining  

Seyoon Jang (Research Institute of Human Ecology, Seoul National University)
Ha Youn Kim (Dept. of Clothing & Textiles, Kunsan National University)
Songmee Kim (Dept. of Textiles, Merchandising, and Fashion Design, Seoul National University)
Woojin Choi (Dept. of Textiles, Merchandising, and Fashion Design, Seoul National University)
Jin Jeong (Dept. of Textiles, Merchandising, and Fashion Design, Seoul National University)
Yuri Lee (Dept. of Textiles, Merchandising, and Fashion Design, Seoul National University/Research Institute of Human Ecology, Seoul National University)
Publication Information
Journal of the Korean Society of Clothing and Textiles / v.46, no.6, 2022 , pp. 1142-1160 More about this Journal
Abstract
Text data plays a significant role in understanding and analyzing trends in consumer, business, and social sectors. For text analysis, there must be a corpus that reflects specific domain knowledge. However, in the field of fashion, the professional corpus is insufficient. This study aims to develop a taxonomy and thesaurus that considers the specialty of fashion products. To this end, about 100,000 fashion vocabulary terms were collected by crawling text data from WSGN, Pantone, and online platforms; text subsequently was extracted through preprocessing with Python. The taxonomy was composed of items, silhouettes, details, styles, colors, textiles, and patterns/prints, which are seven attributes of clothes. The corpus was completed through processing synonyms of terms from fashion books such as dictionaries. Finally, 10,294 vocabulary words, including 1,956 standard Korean words, were classified in the taxonomy. All data was then developed into a web dictionary system. Quantitative and qualitative performance tests of the results were conducted through expert reviews. The performance of the thesaurus also was verified by comparing the results of text mining analysis through the previously developed corpus. This study contributes to achieving a text data standard and enables meaningful results of text mining analysis in the fashion field.
Keywords
Text mining; Taxonomy; Corpus; Thesaurus; Big data;
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