The advent of intelligent computing has brought about a new research trend that focuses on utilizing big data for fashion brand connotation mining and value promotion. The purpose of this study was to investigate consumer emotional trends towards various types of clothing in five popular women's clothing brands: UNIQLO, HSTYLE, VERO MODA, PeaceBird, and ONLY. To achieve this, we collected a total of 93,550 characters of consumer evaluations and employed the Kismet sentiment analysis engine to analyze the emotional polarity of different types of clothing. The results indicated that the emotional polarity of different brands varied significantly, with HSTYLE hoodies, ONLY knitwear, Peacebird pure cotton, and Uniqlo knitting evoking the strongest positive emotions in consumers, respectively. Additionally, this study revealed the most popular garment types and wearing effects in each brand, providing crucial insights for fashion companies to devise effective marketing strategies and enhance their product offerings. Based on these findings, sentiment analysis could be applied in the gaming industry to understand players' emotional responses to different gaming brands, genres, and gameplay, aiding in the development of game promotion strategies and product design. Overall, the findings of this study demonstrate the potential of big data in design and underscore the importance of leveraging it to gain a competitive advantage in the industry.