Study on Clustering Method for Internet Public Opinion Hotspot Topic
Zhenpeng Liu · Journal of Chinese Computer Systems · 2013
The main algorithms for hotspot tracking adopt the Text Clustering technology.When dealing with mass web pages,it is difficult to cluster the expected hotspot.Clustering causes huge central bias.The w orking range of current Internet public opinion monitoring and w arning system is limited by the keyw ords given by the user,thus causing the system not to detect those unexpected events.Based on the characteristics of Internet public opinion occurrence and its spreading,the information acquisition strategy is improved;according to the distinct themes of the titles of the related Internet public opinion's w eb texts,to pursue hotspot keyw ords automatically and to conduct topic clustering based on keyw ord is proposed;based on the different features of new s,forums and blogs,hotspot analysis models of Internet public opinion are designed respectively;On this basis,an internet public opinion monitoring system is designed and implemented.The running tests show that this scheme is capable of finding hot topics timely and tracking realtime emergent events.