An Analysis and Modeling Method for Design Information Based on User Generated Reviews Mining and Extenics

Abstract

To address the inapplicability of data classification results in industrial design, and the lack of theoretical research on design information analysis methods, a design information analysis method based on user experience data was proposed. User experience positive and negative user experience datasets were established through user experience data mining and applying text classification algorithms to perform initial processing of the data. Design features are obtained by extracting effective information from the data, and a design feature analysis is established by combining with the extension class element theory model. Effective information about users and products in the data is classified and analyzed; a conjugate relation between user information and product information is studied, and a design information correlation transformation model is established based on the extension compound theory. Finally, user and product information correlation analyses are combined to obtain an effective product design strategy. The effectiveness of this method is verified by taking electric scooters used by the elderly as an example.