A Web-based Online Document Classification System for Food contamination News – Take Melamine as the Example

• Main Authors: Chia-Yi Lin, 林佳頤
• Other Authors: 徐建業
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/67993068737518264946

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 97 === Food safety is essential to human health, and also to guard the food safety as well as the quality of food is an important issue. As long as the international food and food materials trade being more frequently, the flow of them are far-reaching, and will spread all over the world once they get contaminated. Turns out, it will bring unrest and panic to the public (such as the 2008 outbreak of melamine food contamination). This study is to build an online food contamination monitoring and automatic classification system to regularly search Google news about food contamination (we took melamine-related news as an example) in Taiwan and obtain the most complete information and stored them in the database. The system will classify news into correct categories and can help users find relevant information. In this study, we used Gibbs LDA++, which is a C/C++ implementation of Latent Dirichlet Allocation (LDA) to train news documents by unsupervised learning and supervised learning. The classifier was built by the parameter estimations and inferences from LDA training results and then adjusted manually by human expert. We defined the melamine news as five categories including "contamination", "analysis", "medical and health", "law and policy" and “others”. 521 news documents were used as training data to train the classifier and 793 documents were used to test and evaluate the classifier. The assessment of the effectiveness for the classifier is based on precision, recall and F-measure. According to the evaluation for the classifier, the macro-precision is 69.66%, the macro-recall is 64.52% and the F-measure is 0.68. According to the evaluation results, we estimate the performance of classification system and will improve the system in the future research. We expect the system could save time for reading complexity news, and help people get prepared for food contamination.