An efficient topic-specific web text filtering framework

In this paper, an efficient topic-specific Web text filtering framework is proposed. This framework focuses on blocking some topic-specific Web text content. In this framework, a hybrid feature selection method is proposed, and a high efficient filtering engine is designed. In training, we select features based on CHI statistic and rough set theory, then to construct filter with Vector Space Model. We train our frame with huge datasets, and the result suggests our framework is more effective for the topic-specific text filtering. This framework runs at server such as gateway, and it is more efficient than a client-based system.