MR-LDA: An Efficient Topic Model for Classification of Short Text in Big Social Data

作者：Pang Xiongwen; Wan Benshuai; Li Huifang; Lin Weiwei

来源：International Journal of Grid and High Performance Computing, 2016, 8(4): 100-113.

DOI：10.4018/IJGHPC.2016100106

• Effective solution
• Generation process
• Gibbs sampling methods
• Latent Dirichlet allocation
• Latent dirichlet allocations
• Micro-blog
• Topic minings
• Topic Modeling

Latent Dirichlet Allocation(LDA) is an efficient method of text mining, but applying LDA directly to Chinese micro-blog texts will not work well because micro-blogs are more social, brief, and closely related with each other. Based on LDA, this paper proposes a Micro-blog Relation LDA model (MR-LDA), which takes the relations between Chinese micro-blog documents and other Chinese microblog documents into consideration to help topic mining in micro-blog. The authors extend LDA in the following two points. First, they aggregate several Chinese micro-blogs as a single micro-blog document to solve the problem of short texts. Second, they model the generation process of Chinese micro-blogs more accurately by taking relationship between micro-blog documents into consideration. MR-LDA is more suitable to model Chinese micro-blog data. Gibbs sampling method is borrowed to inference the model. Experimental results on actual datasets show that MR-LDA model can offer an effective solution to text mining for Chinese micro-blog.

• 出版日期2016-12
• 单位华南师范大学; 华南理工大学