A Study on the Perception of Metaverse Fashion Using Big Data Analysis

Abstract

As changes in social and economic paradigms are accelerating, and non-contact has become the new normaldue to the COVID-19 pandemic, metaverse services that build societies in online activities and virtual reality are spread-ing rapidly. This study analyzes the perception and trend of metaverse fashion using big data. TEXTOM was used toextract metaverse and fashion-related words from Naver and Google and analyze their frequency and importance. Addi-tionally, structural equivalence analysis based on the derived main words was conducted to identify the perception andtrend of metaverse fashion. The following results were obtained: First, term frequency(TF) analysis revealed the mostfrequently appearing words were “metaverse,” “fashion,” “virtual,” “brand,” “platform,” “digital,” “world,” “Zepeto,” “com-pany,” and “game.” After analyzing TF-inverse document frequency(TF-IDF), “virtual” was the most important, followedby “brand,” “platform,” “Zepeto,” “digital,” “world,” “industry,” “game,” “fashion show,” and “industry.” “Metaverse” and“fashion” were found to have a high TF but low TF-IDF. Further, words such as “virtual,” “brand,” “platform,” “Zepeto,”and “digital” had a higher TF-IDF ranking than TF, indicating that they had high importance in the text. Second, con-vergence of iterated correlations analysis using UNICET revealed four clusters, classified as “virtual world,” “metaversedistribution platform,” “fashion contents technology investment,” and “metaverse fashion week.” Fashion brands arehosting virtual fashion shows and stores on metaverse platforms where the virtual and real worlds coexist, and invest-ment in developing metaverse-related technologies is under way.