Text mining analysis of terms and information on product names used in online sales of women’s clothing 텍스트마이닝을 활용한 온라인 판매 여성 의류 상품명에 나타난 용어 및 정보분석
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In this study, text mining was conducted on the product names of skirts, pants, shirts/ blouses, and dresses to analyze the characteristics of keywords appearing in online shopping product names. As a result of frequency analysis, the number of keywords that appeared 0.5% or more for each item was around 30, and the number of keywords that appeared 0.1% or more was around 150. The cumulative distribution rate of 150 terms was around 80%. Accordingly, information on 150 key terms was analyzed, from which item, clothing composition, and material information were the found to be the most important types of information (ranking in the top five of all items). In addition, fit and style information for skirts and pants and length information for skirts and dresses were also considered important information. Keywords representing clothing composition information were: banding, high waist, and split for skirts and pants; and V-neck, tie, long sleeves, and puff for shirts/blouses and dresses. It was possible to identify the current design characteristics preferred by consumers from this information. However, there were also problems with terminology that hindered the connection between sellers and consumers. The most common problems were the use of various terms with the same meaning and irregular use of Korean and English terms. However, as a result of using co-appearance frequency analysis, it can be interpreted that there is little intention for product exposure, so it is recommended to avoid it.
I. Introduction
Ⅱ. Review of Literature
1. Characteristics of clothing product names
2. Text mining analysis in fashion
Ⅲ. Research Methods
1. Data crawling and refinement
2. Analysis method
Ⅳ. Results and Discussion
1. Frequency analysis
2. Frequent term by information type
3.Use of various synonyms and product exposure
Ⅴ. Conclusion
References