In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Commerce, Data mining, Design, K-means clustering, Design demands, Emotional orientation matching, Emotional orientations, Network users, Orientation matching, Shopping websites, Short text mining, Short texts, Text-mining, User reviews, Laptop computers
Z. Xiong, Z. Yan, H. Yao, and S. Liang, "Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites", Information, vol. 13, no. 3, p. 110, Feb. 2022, doi: 10.3390/info13030110.