A Self-Adaptive Microblog Topic Tracking Method by User Relationship
BAI Wen-yan1,2, ZHANG Chuang1, XU Ke-fu1, ZHANG Zhi-ming3
1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;
2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
3. Information Technologies Co, (Beijing)Ltd, Beijing 100089, China
Considering the colloquial,short text and other characteristics of microblog and deficiencies in research of it,this article proposes a self-adaptive topic tracking method of microblog by user relationship.First of all,during the tracking time window,the candidate tweet set is mapped into feature space.Secondly,aiming at the characteristic of tweet distribution and the purpose of topic tracking,the paper converts the tweets' feature space.Based on this operation,a binary clustering on tweets set can be constructed by improved K-means clustering algorithm.The yielded relative collection is the target model of the current topic.The experiments with the data extracted from Twitter,show that this method can track down the trend of hot topics and the evolution of focuses in real time,and improve the stability of topic tracking in microblog.This method serves well for user recommendation and public opinion analysis.
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2017, Vol. 45 
