TOY SAFETY SURVEILLANCE FROM ONLINE REVIEWS
| Autor: | Richard Gruss, Alan S. Abrahams, Johnathan P. Ehsani, Matt Winkler |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Safety surveillance
Information Systems and Management Computer science business.industry Sentiment analysis Internet privacy Advertising 02 engineering and technology Gateway (web page) Article Management Information Systems Arts and Humanities (miscellaneous) 020204 information systems 0202 electrical engineering electronic engineering information engineering Developmental and Educational Psychology 020201 artificial intelligence & image processing Narrative business Word (computer architecture) Information Systems |
| Popis: | 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. A method for discovering danger words indicative of toy safety defects is proposed.The danger word list is contrasted with sentiment analysis approaches.Over one million online toy reviews are scored using different methods.Sentiment analysis is found to have low specificity vs. danger word lists.The danger word list is effective in finding reviews mentioning safety concerns. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|