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Supervised topic models utilize document's side information for discoveringpredictive low dimensional representations of documents. Existing models applythe likelihood-based estimation. In this paper, we present a general frameworkof max-margin supervised topic models for both continuous and categoricalresponse variables. Our approach, the maximum entropy discrimination latentDirichlet allocation (MedLDA), utilizes the max-margin principle to trainsupervised topic models and estimate predictive topic representations that arearguably more suitable for prediction tasks. The general principle of MedLDAcan be applied to perform joint max-margin learning and maximum likelihoodestimation for arbitrary topic models, directed or undirected, and supervisedor unsupervised, when the supervised side information is available. We developefficient variational methods for posterior inference and parameter estimation,and demonstrate qualitatively and quantitatively the advantages of MedLDA overlikelihood-based topic models on movie review and 20 Newsgroups data sets.

MedLDA: A General Framework of Maximum Margin Supervised Topic Models