A product affective properties identification approach based on web mining in a crowdsourcing environment

Abstract

Affective product design, which aims to satisfy customer feelings as an aspect of product quality, has attracted more and more research attention. However, conventional product design relies on surveys and user experiments to collect user evaluations, which leads to the issues that (i) consumers can only express their feelings on design attributes specified by assigners; (ii) abundant online consumer resources are neglected; and (iii) a lack of further prioritisation and re-construction of affective design properties. This study aims to develop a product affective properties identification approach. Crowdsourcing platforms have the advantage of obtaining large numbers of free consumer comments and have been utilised as data sources. Web mining and text mining are deployed to capture the crowdsourced product review resources. Then product design knowledge hierarchy is established to find design properties, while sentiment analysis was undertaken to identify affections. With the help of domain ontology to connect design properties and corresponding affections, product affective properties can be identified. Furthermore, the identified affective properties are prioritised, so as to assist in design improvement and support decision making. To illustrate the proposed approach, a pilot study on iPhone 7 was conducted, in which influential affective properties have been identified and ranked.