Dynamic Incremental Analysis of Sub-Topic Evolution

There has been increasing interest in the follow-up progress of events because of sustainability and mutual influence of events. Meanwhile, more and more emergent events make it necessary to follow events in an intuitive and efficient way. However, the majority of traditional event analysis is sentence-oriented or topic-oriented which is event extraction or topic detection and tracking. A hierarchical structure of the topic event is constructed according to the research objects and the scope. A dynamic incremental model is presented for analyzing sub-topic dynamics in the topic event, which borrows the ideas of single-pass clustering, multi-category and dynamic incremental model. It is document-oriented and built on temporal property of the topic event, including dynamic threshold selection, similarity smoothing and dynamic incremental strategy. Meanwhile, overall evaluation criteria combinied with χ\+2-test is served for performance analysis. The algorithm is effective and facilitates users to follow the topic event explicitly. Experimental results reported for four well-known topic events in China show that the performance of sub-topic evolution analysis is improved significantly.