  Probabilistic topic models such as latent Dirichlet allocation (LDA) are
popularly used with Bayesian inference methods such as Gibbs sampling to learn
posterior distributions over topic model parameters. We derive a novel measure
of LDA topic quality using the variability of the posterior distributions.
Compared to several existing baselines for automatic topic evaluation, the
proposed metric achieves state-of-the-art correlations with human judgments of
topic quality in experiments on three corpora. We additionally demonstrate that
topic quality estimation can be further improved using a supervised estimator
that combines multiple metrics.
