We propose a new supervised topic model that uses a nonparametric density estimator to model the distribution of real-valued metadata given a topic. The model is similar to Topics Over Time, but replaces the beta distributions used in that model with a Dirichlet process mixture of normals. The use of a nonparametric density estimator allows for the fitting of a greater class of metadata densities. We compare our model with existing supervised topic models in terms of prediction and show that it is capable of discovering complex metadata distributions in both synthetic and real data.
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