A Natural Language Processing Based Approach To Fashion Trend Identification Using Textual Fashion Week Data

Abstract

The recent years have seen remarkable growth in the field of Natural Language Processing (NLP), driven by developments in deep learning and computing capacity. NLP, an umbrella term for a variety of tools and methods, allows computers to understand, translate, and produce human language. The increased accuracy of NLP systems due to an increase in research and business interest has opened doors for its adoption in various fields, including healthcare and customer service. The fashion industry is not exempt from this phenomenon, with businesses seeking help from NLP techniques to understand trends and consumer preferences to stay ahead of the competition. Considering this, this project aims to integrate NLP techniques with Fashion Week data to predict fashion trends for Summer 2024. Fashion Week events, showcasing the latest designs and styles from designers worldwide, set the tone for the months following them. Here, we analyze textual data from fashion week reviews, designer interviews, and online discussion forums to gain valuable insights into upcoming trends and possible consumer preferences. By leveraging NLP, this project aims to uncover latent themes and relationships within Fashion Week data. Techniques like web scraping are used to gather data from multiple sources and NLP techniques like POS tagging and dependency parsing are used to derive insights from the data about trends. This project follows an exploratory route. The implications of this project extend to fashion businesses seeking to understand the changing trends and preferences. By gaining insights from Fashion Week data, businesses can develop product offerings that resonate better with their target audience and promote strategic decision-making in the fast-fashion industry. This will ultimately lead to an improved fashion ecosystem for businesses and consumers