文献类型：期刊文献

英文题名：Construction of a Novel Production Develop Decision Model Based on Text Mined

作者：Wang, Tianxiong[1]; Sun, Xin[1]; Zhou, Meiyu[1]; Gao, Xian[1]

机构：[1] School of Art Design and Media, East China University of Science and Technology, NO. 130, Meilong Road, Xuhui District, Shanghai, 200237, China

年份：2021

卷号：12779 LNCS

起止页码：128

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20213310758730)

语种：英文

外文关键词：Data mining - Genetic algorithms - Pareto principle - Product design

摘要：When users choose a product, they will consider the emotional experience triggered by the product form. The Kansei engineering is considered to be the most reliable and useful method to deal with users’ emotional needs. Therefore, in this study a hybrid method that combines text mining and Kansei engineering is proposed, which have integrated TF-IDF, SD, BPNN, and NSGA-II methods to extract product shape design solutions that meet user multidimensional needs. The TF-IDF is applied to analyze Kansei image factors of the product of user’s review so as to realize the mining of user needs from the perspective of user real online shopping evaluation. Then, the FA is applied to analyze representative Kansei need items. Furthermore, the BPNN is used to identify the relationship between design variables and user demands, so that the prediction model is constructed. The nondominated sorting genetic algorithm-II is used as the multi-objective evolutionary method to obtain the Pareto optimal solutions that meets the user’s multidimensional needs. Taking electric bicycles as an example, the experimental results show that this proposed method can help designers to obtain the production solutions based on users’ real Kansei needs. ? 2021, Springer Nature Switzerland AG.