Hot topic extraction using time window
Huifang Ma · 2011
This paper presents a novel time window based hot topic extraction model for news stream. The model considers both pervasiveness and burst feature of topic terms. Pervasiveness is evaluated by terms' occurrences reported from different channels and burst is assessed by terms' abnormal occurrence frequencies from different time intervals. An energy ratio threshold based approach for burst detection is adopted and time window is introduced for news text stream analysis. TF-PDF is then combined to weigh the terms. Experiment results demonstrate that our model is effective in topic extraction for news texts.