We develop an online variational Bayes (VB) algorithm for Latent
Dirichlet Allocation (LDA).  Online LDA is based on online
stochastic optimization with a natural gradient step, which we show
converges to a local optimum of the VB objective function.  It can
handily analyze massive document collections, including those
arriving in a stream.  We study the performance of online LDA in
several ways, including by fitting a 100-topic topic model to 3.3M
articles from Wikipedia in a single pass.  We demonstrate that
online LDA finds topic models as good or better than those found
with batch VB, and in a fraction of the time.