Feature Extraction Method Based on RLDA Topic Model
FENG Xinqi
ZHANG Kun
REN Yihao
XIE Bin
ZHAO Jing
Abstract：In this paper,to accurately mining micro-blog user interest,the data concerning original,reposted and liked mi-cro-blog content as well as the ranking of all these micro-blogs are collected and analyzed.So the accurate description information of micro-blog users'interests is obtained. Then based on the LDA model,we proposed a modified topic feature extraction model named as Ranking LDA is proposed.In comparison to LDA model,RLDA model includs a new concept-Micro-blog popularity rank-ing to improve the mining accuracy of the micro-blog users'interests.In the process of modeling the RLDA topic model,the con-cepts of hyper-hyper parameters is introduced.Hyper parameters are sampled from dirichlet distribution.Experiments suggest that, compared with the LDA model,RLDA model achieves quite a great promotion on the accuracy of interest mining for micro-blog users.
Keywords：interests miningMicro-blog popularity rankingRanking Latent Dirichlet Allocation modelfeature extrac-tionhyper-hyper parameters
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1980-1985 )