面向食品安全监理话题检测方法的研究  

Research of Topic Detection Method for Food Safety Surveillance

作　　者：冯振海[1] 刘宏志[1] 

机构地区：[1]北京工商大学计算机与信息工程学院,北京100048

出　　处：《食品科学技术学报》2016年第5期89-94,共6页Journal of Food Science and Technology

基　　金：北京市属高等学校科学技术与研究生教育创新工程建设项目(PXM2012_014213_000037)

摘　　要：食品安全问题一直是国民热切关注的话题,关系到社会的多个领域。为及时知晓食品安全领域关注的热点问题,对比了食品安全热点话题与其他热点话题在检测方法上的异同,构建了食品安全监理话题检测模型,运用聚类算法对食品安全数据进行文本挖掘来实现话题检测,并对食品安全数据进行分析。通过实验说明,采用Single-Pass算法的评价优于K-Means算法的评价,能够有效地对食品安全话题进行检测。Food safety problem has been a hot topic of national concern, which related to many areas of society. In order to know hot issues that relate to food safety in timely, food safety hot topics and other hot topics of the similarities and differences in detection methods were compared. The food safety surveil- lance topic detection model was constructed and the clustering algorithm was used for text mining for food safety data to achieve the topic detection. Through the experimental results, the evaluation of the Single- Pass algorithm was better than the K-Means algorithm, which could effectively detect food safety topics.

关 键 词：食品安全监理 文本挖掘 话题检测 

分 类 号：TS201.6[轻工技术与工程—食品科学] R155[轻工技术与工程—食品科学与工程]