期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2012
卷号：12
期号：6
页码：99-106
出版社：International Journal of Computer Science and Network Security
摘要：In this paper, we devise an approach for identifying and classifying contents of interest related to geographic communities from news articles streams. We first conduct a short study on related works, and then present our approach, which consists in 1) filtering out contents irrelevant to communities and 2) classifying the remaining relevant news articles. Using a confidence threshold, the filtering and classification tasks can be performed in one pass using the weights learned by the same algorithm. We use Bayesian text classification, and because of important empiric class imbalance in Web-crawled corpora, we test several approaches: Na?ve Bayes, Complementary Na?ve Bayes, use of {1,2,3}-Grams, and use of oversampling. We find out in our testing experiment on Japanese prefectures that 3-gram CNB with oversampling is the most effective approach in terms of precision, while retaining acceptable training time and testing time.
关键词：Web Intelligence; Natural Language Processing; Machine Learning; Semantic Web
