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.

与玩具相关的伤害占儿童伤害的很大一部分，预防这些伤害仍然是监管机构和制造商的目标。文本挖掘是一种越来越流行的利用大数据揭示单词重要性的方法。这项研究旨在确定文本挖掘在发现潜在危险儿童玩具方面的有效性。我们开发了一个危险词列表，也称为“烟雾词”列表，来自伤害和回忆文本叙述。然后，我们使用烟雾词列表对超过一百万条亚马逊评论进行评分，最高分表示潜在的安全问题。我们在单词重叠和有效性方面将烟雾词列表与传统的情感分析技术进行比较。我们发现烟雾词列表与传统的情感词典有很大不同，并为识别儿童玩具评论中的安全问题提供了一种具有统计意义的方法。我们的研究结果表明，文本挖掘实际上是监测儿童玩具安全问题的有效方法，并且可能成为有效预防玩具产品相关伤害的途径。