Quantification of Design Parameters through Analysis of User-Generated Contents

The growth of Web 2.0 has facilitated the sharing of opinions and experiences gained from products and services. Furthermore, the development of artificial-intelligence technology has enabled the extraction of various information from these user-generated content (UGC) data. This paper reports on the extraction of useful design parameters by processing UGC data written in natural language. The information collection method and quantification model of the target product to be analyzed are presented, and the natural-language-processing process is described. Although such quantifications have not been actively performed, they can help reduce the complexity of design and learning for novice designers. The necessity and significance of this method are illustrated using cases from outside Korea.