Utilizing text mining and Kansei Engineering to support data-driven design automation at conceptual design stage

Purpose: The purpose of this research was to develop a case-based method for analyzing online customer reviews and extracting customer preferences through an integration of text mining and Kansei Engineering (KE) in an effort to achieve conceptual data-driven design automation and to successfully identify future trends in a particular consumer product. Design/Methodology/Approach This study's model merges text mining and KE to extract key descriptive Kansei terminology according to actual customer reviews and use it to forecast consumer preferred product design while reducing certain repetitious tasks of designers. This work first collects online product reviews using text mining. Then, through the application of KE, the customer-preferred design components are identified and incorporated into the product design specifications. Finally, an Application Programming Interface (API) is developed to automatically generate a CAD preliminary design. Case Study A road bike case study is provided to demonstrate the practicality of proposed method. The online reviews are collected from Amazon.com. The related design elements are classified into six key components which can be modified in the proposed conceptual design automation system. Originality/value This is the first paper that has applied text mining and KE for use in product development. This work can also reduce the time and cost of product design through the automation of repetitive design tasks. The conceptual design automation system is valuable for designers wishing to identify customer needs and to generate engineering drawings in a timely manner without significant repetition during the design process.