رویکرد تشخیص خصوصیات موثر محصول مبتنی بر وب کاوی در محیط جمع سپاری A product affective properties identification approach based on web mining in a crowdsourcing environment

1. Introduction Nowadays, the increasingly competitive market has elicited an urgent need to develop successful products that can satisfy the increasing consumer expectations and demands (Cross 2000). Apart from basic functions and economic considerations, affective aspects of products are also of great concern to consumers (Jordan 1998; Khalid 2006) Design attributes, such as colour and form can provoke feelings, and influence the overall perception of a product. Therefore, affective product design (APD) was advocated to develop products that satisfy customer feelings as an aspect of product quality (Nagamachi 2002; Lai, Chang, and Chang 2005) In this regard, in the relevant literature, it is indicated that products with deliberate affective design can help improve consumer satisfaction and further promote product success (Seva, Duh, and Helander 2007) Therefore, good affective features can sharpen the competitive edge of products, and a precise understanding of product affective properties appears particularly important and deserves in-depth investigation. Regarding previous affective design studies, most research focus was placed on the analysis of emotions (Pengnate and Sarathy 2017; Gennaria et al. 2017; Huang, Chen, and Khoo 2012) and the establishment of quantitative models linking emotions and design attributes (Nagamachi 2002; Park and Han 2004; Zhai, Khoo, and Zhong 2009) In terms of data acquirement methods, surveys and user experiments are widely adopted. As in (Huang, Chen, and Khoo 2012; Huang, Chen, and Khoo 2012) questionnaires are used as the main method to collect consumer responses. However, the design of a survey, questionnaire or user experiment, to some extent, imposes the constraints of user involvement and user freedom in presenting their feelings on any design properties. Moreover, with the rapid development of the Internet techniques, Web 2.0 enables stronger interaction and participation of Internet users and leads to the participatory web (Hedges and Dunn 2018; Newman et al. 2016) Recently, the debate about Web 3.0 brings about the idea of further merging intelligent webs and web services, and correspondingly, the advancement of intelligent media and social media mining has deepened the connections between web service and users (Newman et al. 2016) New collaboration forms can be facilitated to hear customers’ voice and involve them in product design process (Djelassi and Decoopman 2013) Therefore, Web technologies can provide interactive platforms for enterprises to connect with worldwide consumers, so as to improve customer participation in the product development process and leverage open innovation. For consumers, they have more convenient channels in which to contribute their opinions. In particular, crowdsourcing, which is an important method for drawing on large numbers of people to contribute their opinions (Cross 2000; Howe 2006; Chang, Chen, and Lee 2014) has become an important way to effect consumer responses. Taking Proctor & Gamble as an example, the use of a crowdsourcing platform ‘InnoCentive’ to collect product problems and possible solutions from Internet users has helped them increase the problem solving rate to 30%. For more examples, Wikipedia, Amazon’s Mechanical Turk and iStockPhoto.com take advantage of the tremendous numbers of Web users that are willing to contribute their knowledge and ideas. Crowdsourcing appears to be a promising way to solicit consumer responses and is studied as the main data source in this work. Considering that large numbers of consumers’ comments are collected via crowdsourcing and are often presented by textual data, data mining techniques, which are efficient in dealing with big data and effective for textual analysis are considered.