Bursty topics extraction for web forums
You Chen, Sen Xiang Yang, Xueqi Cheng · 2009
Many bursty topics which are difficult to summarize and search exist in web forums. Most existing topic detection and tracking (TDT) methods deal with the news stories, but the language used in web forums are much casual, oral and informal compared with news data. In this paper, we present a noise-filtered model to extract bursty topics from web forums using terms and participations of users. Conducting experiments in ShuiMu community we demonstrate the efficiency of our model. Our model not only extracts bursty topics which are better organized for search and visualization, but also discoveries communities corresponding to these topics.