  Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has
become the most popular algorithm for aspect modeling. While sufficiently
successful in text topic extraction from large corpora, VB is less successful
in identifying aspects in the presence of limited data. We present a novel
variational message passing algorithm as applied to Latent Dirichlet Allocation
(LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In
situations where marginalisation leads to non-conjugate messages, we use ideas
from sampling to derive approximate update equations. In cases where conjugacy
holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is
used. Our algorithm, ALBU (approximate LBU), has strong similarities with
Variational Message Passing (VMP) (which is the message passing variant of VB).
To compare the performance of the algorithms in the presence of limited data,
we use data sets consisting of tweets and news groups. Additionally, to perform
more fine grained evaluations and comparisons, we use simulations that enable
comparisons with the ground truth via Kullback-Leibler divergence (KLD). Using
coherence measures for the text corpora and KLD with the simulations we show
that ALBU learns latent distributions more accurately than does VB, especially
for smaller data sets.
