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

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.

KEYWORDS:

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

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

W. M. Wang http://orcid.org/0000-0003-1188-4537

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under [grant number 51405089]; Natural Science Foundation of Guangdong Province under [grant number 2018A0303130035]; and China Postdoctoral Science Foundation under [grant number 2018M630928] and [grant number 2018M633008].