Supporting the construction of affective product taxonomies from online customer reviews: an affective-semantic approach

Wai Ming Wang, Z.G. Tian, Z. Li, J.W. Wang, Ali Vatankhah Barenji, Mei Na Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Consumers today not only consider the functionality and reliability of products, but also concern with affective aspects of products to meet their emotional needs. Products with good affective design can excite consumers’ psychological feelings and enhance consumer satisfaction. Affective engineering aims to discover relationships between product features and affective preferences for affective design. Traditional methods rely heavily on manual surveys, which are costly, and the affective design knowledge is difficult to share and update. There is a need to develop an efficient way to build a common knowledge representation. In this paper, we propose an affective-semantic approach to automatically construct affective product taxonomy based on online consumer reviews. We incorporate affective engineering and semantic analysis to extract product features and affective attributes from online product information. We construct taxonomy by relating the extracted product features and affective attributes based on their meaning. To evaluate the effectiveness of the approach, experiments have been conducted using public available data. The results showed that the approach can effectively identify and measure affective information. It could help develop a common understanding of the design domain for reuse and expansion. It could also assist product designers review existing products based on affective aspects and consumer perspectives.
Original languageEnglish
Pages (from-to)445-476
JournalJournal of Engineering Design
Volume30
Issue number10-12
DOIs
Publication statusPublished - 18 Jul 2019

Keywords

  • Semantic analysis
  • Kansei engineering
  • affective product taxonomy
  • opinion mining
  • affective design

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