  We introduce supervised latent Dirichlet allocation (sLDA), a statistical
model of labelled documents. The model accommodates a variety of response
types. We derive an approximate maximum-likelihood procedure for parameter
estimation, which relies on variational methods to handle intractable posterior
expectations. Prediction problems motivate this research: we use the fitted
model to predict response values for new documents. We test sLDA on two
real-world problems: movie ratings predicted from reviews, and the political
tone of amendments in the U.S. Senate based on the amendment text. We
illustrate the benefits of sLDA versus modern regularized regression, as well
as versus an unsupervised LDA analysis followed by a separate regression.
