Affective Analysis of Materials Choice in Clothing Based on Network Semantics and Big Data

Abstract

Consumer emotion analysis is one of the rising research topics in the retail clothing materials market. The e-commerce internet platform is becoming an enormous data resource, which can be collected and evaluated for the consumer’s preference. Here, we collected the online comments for clothing materials and applied the Software Kismet Engine to perform the big data semantic cluster analysis. Different materials’ design elements were analyzed and associated with the affective elements of the customers. The emotional words in the comment text are extracted and categorized. Such association and categorization between clothing materials and design elements based on consumer’s emotion analysis can be very important and helpful for choosing the materials in the retail market.