TM-ToT: An Effective Model for Topic Mining from the Tibetan Messages

The microblog platforms, such as Weibo, now accumulate a large scale of data including the Tibetan messages. Discovering the latent topics from such huge volume of Tibetan data plays a significant role in tracing the dynamics of the Tibetan community, which contributes to uncover the public opinion of this community to the government. Although topic models can find out the latent structure from traditional document corpus, their performance on Tibetan messages is unsatisfactory because the short messages cause the severe data spasity challenge. In this paper, we propose a novel model called TM-ToT, which is derived from ToT (Topic over Time) aiming at mining latent topics effectively from the Tibetan messages. Firstly, we assume each topic is a mixture distribution influenced by both word co-occurrences and messages timestamps. Therefore, TM-ToT can capture the changes of each topic over time. Subsequently, we aggregate all messages published by the same author to form a lengthy pseudo-document to tackle the data sparsity problem. Finally, we present a Gibbs sampling implementation for the inference of TM-ToT. We evaluate TM-ToT on a real dataset. In our experiments, TM-ToT outperforms Twitter-LDA by a large margin in terms of perplexity. Furthermore, the quality of the generated latent topics of TM-ToT is promising.