Extracting Overlapping Topics from Micro-Blog Based on Mixture Model
Yan Zhu Yang · Jisuanji kexue yu tansuo · 2013
Micro-blog is a new platform to share and disseminate information quickly. It is characterized by huge amount of scattered and diverse information. The most of traditional topics extraction algorithms are partitioning method, which do not consider the relationship between the topics, so there are some limitations. This paper focuses on the task of news topics extraction from large-scale short posts of micro-blog service. The word segmentation is processed according to the characteristics of the micro-blog text using the Chinese word segmentation software with high accuracy and ambiguity recognition, which is developed by Institute of Noetics and Wisdom, Southwest Jiaotong University. And then, this paper proposes an overlapping topic detection algorithm based on mixture model. The experimental results prove the feasibility and validity of the algorithm.