Abstract: In recent years,as an emerging social network,Microblog is being used by a lot of users.The micro blogging platform contains a large amount of information and the update speed of the information is fast so that it often makes users can not find the information they need.Then,it is important to help users find the information which is sent by people who have a great influence.The content that the micro blogging platform publishes and updates is very fast,and the content is not standardized,so that the timeliness and authoritativeness tend to be more important.The purpose of research on influence of micro blogging users in this paper is to identify the influence of different users in different areas.This paper divided the users into different areas by using two different features which are the content of the micro blogging and the concerned relationship of the users.During this,we used the parallel new word recognition algorithm in the micro blogging content and semantic extensions on some important text feature to improve accuracy of the dividing result.Then we computed the influence of the users in all the classification using three models,and combined the result in different weight.At last,we tested the accuracy of the result and compared it with other ways.

Key words: Sina microblog,Field division,Influence,Text analysis,New word recognition