A HMM_based hot topic lifecycle prediction model
Ruifang Liu, Jun Wang, Meng Zhang · 2011
Web documents can be clustered into topics with topic detection and tracking(TDT) technologies. With the topics' data collected by TDT system, it is found that the lifecycle of topics has 4 stages. In the paper a HMM-based state prediction model for topics is proposed. Some topics with similar lifecycles share a same model, several models are trained with history data of topics, these models are used for new topic state prediction. Experiment results show the performance of the Forward Probability Prediction Algorithm, and the comparison with other method is analyzed. It will be useful for the department who concern the hot topic monitoring.