基于SOM 聚类的微博话题发现.

With the increase of microblog users, the information of microblog platform is updating frequently. This paper proposed microblog topics detection based on SOM clustering for the features of the microblog text data sparseness, new words and non-standard words. Firstly, it pretreated the short texts from the primitive text corpus, and extracted the features of the short texts by the word vector model which reduced the computational burden caused by the high vector dimension. In order to reduce the large amount of computation just to the high vector dimensions, this paper extracted the short text feature extraction by word vector model. Then, the topic clustering could be achieved by an improved SOM clustering. The algorithm improved the traditional texts clustering shortcoming. And the algorithm could find the topic effectively. Experimental results show that the algorithm' s comprehensive index F value is improved obviously than the traditional methods. [ABSTRACT FROM AUTHOR]