Exploring online reviews for user experience modeling
Editor: Udo Lindemann, Srinivasan V, Yong Se Kim, Sang Won Lee, John Clarkson, Gaetano Cascini
Author: Liang, Yan; Liu, Ying; Loh, Han Tong
Institution: Mechanical Engineering, National University of Singapore, Singapore
In the market-driven design paradigm which aims to serve customers with attractive user experience (UX), one of the important stages is to understand customerâs feelings about products. Traditional techniques like questionnaire remain important approaches to collect data for UX analysis. However, the data captured are often limited and incremental costs are needed to acquire userâs changeable experiences over time. In addition, with the wide use of social software, customers have generated increasing amount of online reviews to share their opinions. In this paper, we aim to investigate whether online reviews are suitable data sources for UX analysis and how useful reviews can be surfaced. Firstly, by considering UX elements and data processing, a faceted-based UX model is proposed. We then measure review content from several aspects, such as richness and diversity, and propose scoring methods to identify useful reviews. Using Amazon reviews as research data, we have reported our experiments on issues like a brief example of useful reviews suggested by the proposed methods, situation-based features concerned by customers and some key product features generated from reviews.