Trend Detection in fashion via Text Analytics

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.