Improved LDA model for microblog topic mining
Improved LDA model for microblog topic mining
复制标题
DOI：
--
复制
发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Xie Hao;Jiang Hong
中科院分区：
文献类型：
--
作者：
Xie Hao;Jiang Hong
随着新浪微博用户的剧增，微博网站已经成为广大用户获取信息的平台。由于微博是一种特殊的文本，长度有限，传统的主题模型不能很好地分析微博内容。本文提出了一种基于LDA的微博生成模型RT-LDA，并采用Gibbs抽样对该模型进行推导，该模型不仅可以准确地挖掘出每条微博的主题，还可以归纳出相关主题的分布。通过在真实数据上的实验，验证了RT-LDA在微博主题挖掘中的有效性。
With the dramatic increase of Sina microblog users.microblog websites have been the platforms for a wide spectrum of users to get information.Due to the fact that microblog is a special kind of text with the restricted length,traditional topic models could not be used to analyze the microblog content very well.RT-LDA,a microblog generation model based on LDA is proposed in this paper.Gibbs sampling is chosen to deduce the model,which can not only mine the topics of each microblog accurately but also induce the distribution of the concerned topics. RT-LDA's effective utility on topic mining of the microblogs is verified by the experiments on real data.