SimLDA: A tool for topic model evaluation

TL;DR

SimLDA introduces a novel variational message passing algorithm called ALBU for topic modeling, which outperforms traditional Variational Bayes in small data scenarios by more accurately learning latent distributions.

Contribution

The paper presents ALBU, a new variational message passing algorithm for LDA, improving topic modeling accuracy in limited data situations compared to existing methods.

Findings

ALBU outperforms VB in small data sets based on coherence measures.

ALBU provides more accurate latent distribution estimates in limited data scenarios.

The algorithm bridges ideas from sampling and conjugacy-based message passing.

Abstract

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. Using coherence measures we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets.