A nonparametric mixture model for topic modeling over time [chapter]

Avinava Dubey, Ahmed Hefny, Sinead Williamson, Eric P. Xing
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to change over time. A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal variation, or require computationally expensive inference methods. In this paper we propose nonparametric Topics over Time (npTOT), a model for time-varying topics that allows an
more » ... unded number of topics and flexible distribution over the temporal variations in those topics' popularity. We develop a collapsed Gibbs sampler for the proposed model and compare against existing models on synthetic and real document sets.
doi:10.1137/1.9781611972832.59 dblp:conf/sdm/DubeyHWX13 fatcat:65qhb6yup5emzbmtddzs2ytthm