Automated defect discovery for dishwasher appliances from online consumer reviews. (January 2017)

• Record Type: Journal Article
• Authors: Law, Darren; Gruss, Richard; Abrahams, Alan S.
• Is Part Of: Expert systems with applications. Volume 67(2017)
• Page Start: 84
• Page End: 94
• Publication Date: 2017-01
• Subjects: Defect discovery -- Text mining -- Quality management; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.08.069
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Online dishwasher reviews contain many postings relating to dishwasher defects. We assess the effectiveness of sentiment analysis for dishwasher defect discovery. We propose new smoke term dictionaries for enhancing dishwasher defect discovery. Smoke terms deliver comparable performance to the best sentiment-based technique. Dishwasher smoke terms are distinct from sentiment terms, and mainly non-emotive. Abstract: Product defects can have a devastating impact on a firm's sales and reputation, especially in the era of social media. The early detection of defects could not only protect consumers from financial losses, but could also mitigate financial damage to the manufacturer. Previous work in automated defect discovery has had success in the automotive, consumer electronics, and toy industries, but so far there has been no application to home appliances. In this study, we extend the text analytic framework conceived in earlier work to the discovery of underperformance in large home appliances, specifically dishwashers. We find that generic cross-domain sentiment techniques can be strongly complemented by domain-specific "smoke" and "sparkle" term lists that are highly correlated with potential defects. These findings can be highly beneficial to improving dishwasher appliance quality management methods.