Online Archive of University of Virginia Scholarship
Trend Detection in fashion via Text Analytics197 views
Author
Xiao, Tianxiao, Computer Science - School of Engineering and Applied Science, University of Virginia
Advisors
Ji, Yangfeng, EN-Comp Science Dept, University of Virginia
Foutz, Y Natasha, MC-Dean's Admin, University of Virginia
Abstract
Fashion trend prediction is a complex problem due to a vast number of latent drivers and fashion products' highly temporal and dynamic characteristics. It requires a reliable and time-sensitive means to detect fashion trend. Analyzing social media data is well-suited for the task of trend detection from the consumer perspective. In this project, I leverage text analytics on time-series social media data. I first collected data across multiple categories of fashion over a span of ten years via mining, API and third-party sources. Then I extract fashion related keywords and conduct yearly and quarterly trend detection via keyword clustering and topic modeling. The ultimate goal is to discover hidden semantic structures in a text body at scale and detect types and drivers of different fashion life cycles, such as classic, cyclical, and fad.
Xiao, Tianxiao. Trend Detection in fashion via Text Analytics. University of Virginia, Computer Science - School of Engineering and Applied Science, MS (Master of Science), 2021-04-28, https://doi.org/10.18130/apmd-z028.
