Large scale microblog mining using distributed MB-LDA

In the information explosion era, large scale data processing and mining is a hot issue. As microblog grows more popular, microblog services have become information provider on a web scale, so researches on microblog begin to focus more on its content mining than solely user's relationship analysis before. Although traditional text mining methods have been studied well, no algorithm is designed specially for microblog data, which contain structured information on social network besides plain text. In this paper, we introduce a novel probabilistic generative model MicroBlog-Latent Dirichlet Allocation (MB-LDA), which takes both contactor relevance relation and document relevance relation into consideration to improve topic mining in microblogs. Through Gibbs sampling for approximate inference of our model, MB-LDA can discover not only the topics of microblogs, but also the topics focused by contactors. When faced with large datasets, traditional techniques on single node become less practical within limited resources. So we present distributed MB-LDA in MapReduce framework in order to process large scale microblogs with high scalability. Furthermore, we apply a performance model to optimize the execution time by tuning the number of mappers and reducers. Experimental results on actual dataset show MB-LDA outperforms the baseline of LDA and distributed MB-LDA offers an effective solution to topic mining for large scale microblogs.