View : 756 Download: 0

Analyzing genderless fashion trends of consumers’ perceptions on social media: using unstructured big data analysis through Latent Dirichlet Allocation-based topic modeling

Title
Analyzing genderless fashion trends of consumers’ perceptions on social media: using unstructured big data analysis through Latent Dirichlet Allocation-based topic modeling
Authors
Kim H.Cho I.Park M.
Ewha Authors
박민정조인호
SCOPUS Author ID
박민정scopusscopus
Issue Date
2022
Journal Title
Fashion and Textiles
ISSN
2198-0802JCR Link
Citation
Fashion and Textiles vol. 9, no. 1
Keywords
Fashion big data analysisGenderless fashion trendLatent Dirichlet Allocation-based topic modelingSocial network analysisText-mining
Publisher
Springer
Indexed
SCIE; SCOPUS; KCI WOS scopus
Document Type
Article
Abstract
After the development of Web 2.0 and social networks, analyzing consumers’ responses and opinions in real-time became profoundly important to gain business insights. This study aims to identify consumers’ preferences and perceptions of genderless fashion trends by text-mining, Latent Dirichlet Allocation-based topic modeling, and time-series linear regression analysis. Unstructured text data from consumer-posted sources, such as blogs and online communities, were collected from January 1, 2018 to December 31, 2020. We examined 9722 posts that included the keyword “genderless fashion” with Python 3.7 software. Results showed that consumers were interested in fragrances, fashion, and beauty brands and products. In particular, 18 topics were extracted: 13 were classified as fashion categories and 5 were derived from beauty and fragrance sectors. Examining the genderless fashion trend development among consumers from 2018 to 2020, “perfume and scent” was revealed as the hot topic, whereas “bags,” “all-in-one skin care,” and “set-up suit” were cold topics, declining in popularity among consumers. The findings contribute to contemporary fashion trends and provide in-depth knowledge about consumers’ perceptions using big data analysis methods and offer insights into product development strategies. © 2022, The Author(s).
DOI
10.1186/s40691-021-00281-6
Appears in Collections:
신산업융합대학 > 의류산업학과 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

BROWSE