An Effective Algorithm of News Topic Tracking

Topic tracking is to track trend of news topic, which people are interested in. It is a very pragmatic method in information retrieval. Compared with keywords retrieval, topic tracking excels in dynamic tracking based on text model and its content understanding, so it is mostly involved in text expressing and semantic understanding. LS-SVM, as a new method for news topic tracking, is presented in this paper. It analyzes texts using latent semantic analysis, and achieves semantic-based character feature reduction and document expression. SVM is used to complete semantic-based topic tracking. Experiment results show that LS-SVM outperforms conventional methods, and reduces fault and fail rate of topic tracking.