BBS Topic´s Hotness Forecast Based on Back-Propagation Neural Network

Online hot topic detection is a significant research field in web data mining, which can help people make policy decision or benefit to people´s daily life. Actually, in recent years more and more hot topics are arising from BBS, often referred as online forum. BBS provide a communication platform for people to discuss and express their views. It´s obvious that forecasting the hotness topics on BBS is important and meaningful. In this paper we present an approach to predict the hotness of topics based on BPNN (Back-Propagation Neural Network). Text sentiment internet user´s attention and hotspot relative have been considered to forecast the hotness of topics. At the last the experiment results over SINA reading forum show our approach is effective.