Alexandria Engineering Journal (Feb 2023)
An improved Kansei engineering method based on the mining of online product reviews
Abstract
The explosive growth of online review data on e-commerce websites demands major changes to product emotional design. Kansei engineering, the primary approach of product emotional design, faces multiple problems. This study aims to improve Kansei engineering method to adapt to the emotional design trends of the big data era. Targeting the data of online product reviews, the authors mined text data, calculated fine-grained emotions, and presented an innovative design flow for Kansei engineering. Firstly, a crawler program was compiled to mine online product reviews and product parameters from e-commerce websites, and the mined data were processed. Next, a Kansei word pair screening method was proposed based on term frequency – evaluation, potency, activity (TF-EPA), and used to obtain the Kansei word pairs depicting the imagery of product emotions. After that, a fine-grained calculation method for Kansei evaluation value of products was proposed based on term clustering and adverbs of degrees, and utilized to compute the evaluation value of each product sample under each Kansei word pair. Finally, the backpropagation (BP) neural network was extended into a mapping model between product parameters and Kansei evaluation values, and the prediction performance of the model was evaluated. The smart phone was taken as an example to verify the proposed method, The optimal training values of the 6 groups of Kansei word pairs were 0.00061, 0.00053, 0.00065, 0.00038, 0.00065, and 0.00093, respectively. The absolute error mean of 28 test sets in the experiment under 6 groups of Kansei word pairs were 0.0696, 0.1001, 0.0784, 0.0815, 0.1328, and 0.1358, respectively. Taking the third group as an example, the absolute error mean obtained by the multiple linear regression method was 0.1127 greater than 0.0784, which proved that the BP neural network method used in this paper was superior to the multiple linear regression method in accuracy.
