This work has been funded in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI 12 RC 2289 (INSIGHT) and by Enterprise Ireland and the IDA under the Technology Centre Programme [Grant TC-2012-009]
@cite_7 have used Dynamic Hierarchical Dirichlet Process to track topics over time, documents can be exchanged however the ordering is intact. They also applied this to longitudinal (NIPS) papers to track emerging and decaying topics (worth noting for tracking changing topics around compliance issues).
@cite_7
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Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics' distribution and popularity are time-evolving. Several models exist that model the evolution of some but not all of the above aspects. In this paper we introduce infinite dynamic topic models, iDTM, that can accommodate the evolution of all the aforementioned aspects. Our model assumes that documents are organized into epochs, where the documents within each epoch are exchangeable but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the representation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the efficacy of our model on both simulated and real datasets with favorable outcome.