Citation

Winkler M, Abrahams AS, Gruss R, Ehsani JP. Decis. Support Syst. 2016; 90: 23-32.

Affiliation

Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6100 Executive Blvd, Room 7B13K, MSC 7510, Bethesda, MD, 20892-7510, United States.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.dss.2016.06.016

PMID

27942092

Abstract

Toy-related injuries account for a significant number of childhood injuries and the prevention of these injuries remains a goal for regulatory agencies and manufacturers. Text-mining is an increasingly prevalent method for uncovering the significance of words using big data. This research sets out to determine the effectiveness of text-mining in uncovering potentially dangerous children's toys. We develop a danger word list, also known as a 'smoke word' list, from injury and recall text narratives. We then use the smoke word lists to score over one million Amazon reviews, with the top scores denoting potential safety concerns. We compare the smoke word list to conventional sentiment analysis techniques, in terms of both word overlap and effectiveness. We find that smoke word lists are highly distinct from conventional sentiment dictionaries and provide a statistically significant method for identifying safety concerns in children's toy reviews. Our findings indicate that text-mining is, in fact, an effective method for the surveillance of safety concerns in children's toys and could be a gateway to effective prevention of toy-product-related injuries.

Language: en