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Generating user interpretable multi-class predictions in data richenvironments with many classes and explanatory covariates is a daunting task.We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervisedtopic model for multi-class classification that can handle both many classes aswell as many covariates. To handle many classes we use the recently proposedDiagonal Orthant (DO) probit model (Johndrow et al., 2013) together with anefficient Horseshoe prior for variable selection/shrinkage (Carvalho et al.,2010). We propose a computationally efficient parallel Gibbs sampler for thenew model. An important advantage of DOLDA is that learned topics are directlyconnected to individual classes without the need for a reference class. Weevaluate the model's predictive accuracy on two datasets and demonstrateDOLDA's advantage in interpreting the generated predictions.

DOLDA - a regularized supervised topic model for high-dimensional multi-class regression