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