Aiming at the problem that the traditional topic model ignore short text and dynamic evolution of microblog, a Biterm Topic over Time (BToT) model based on microblog text was proposed, and the subject evolution analysis was carried out by the proposed model. A continuous time variable was introduced to describe the dynamic evolution of the topic in the time dimension during the process of text generation in the BToT model, and the "Biterm" structure of the topic sharing in the document was formed to extend short text feature. The Gibbs sampling method was used to estimate the parameters of BToT, and the topic evaluation was analyzed by topic-time distributed parameters. The experimental results on real microblog datasets show that BToT can characterize the latent topic evolution and has lower perplexity than Latent Dirichlet Allocation (LDA), Biterm Topic Model (BTM) and Topic over Time (ToT).

To solve the problems of mining relationships among topics, authors and time in large scale scientific literature corpora, this paper proposed the Author-Topic over Time (AToT) model according to the intra-features and inter-features of scientific literature. In AToT, a document was represented as a mixture of probabilistic topics and each topic was correspondent with a multinomial distribution over words and a beta distribution over time. The word-topic distribution was influenced not only by word co-occurrence but also by document timestamps. Each author was also correspondent with a multinomial distribution over topics. The word-topic distribution and author-topic distribution were used to describe the topics evolution and research interests changes of the authors over time respectively. Parameters in AToT could be learned from the documents by employing methods of Gibbs sampling. The experimental results by running in the collections of 1700 NIPS conference papers show that AToT model can characterize the latent topics evolution, dynamically find authors research interests and predict the authors related to the topics. Meanwhile, AToT model can also lower perplexity compared with the author-topic model.