Recent advances and applications of probabilistic topic models
I. G. Wood · AIP conference proceedings · 2014
I present here an overview of recent advances in probabilistic topic modelling and related Bayesian graphical models as well as some of their more atypical applications outside of their home: text analysis. These techniques allow the modelling of high dimensional count vectors with strong correlations. With such data, simply calculating a correlation matrix is infeasible. Probabilistic topic models address this using mixtures of multinomials estimated via Bayesian inference with Dirichlet priors. The use of conjugate priors allows for efficient inference, and these techniques scale well to data sets with many millions of vectors. The first of these techniques to attract significant attention was Latent Dirichlet Allocation (LDA) [1, 2]. Numerous extensions and adaptations of LDA have been proposed: non-parametric models; assorted models incorporating authors, sentiment and other features; models regularised through the use of extra metadata or extra priors on topic structure, and many more [3]. They have become widely used in the text analysis and population genetics communities, with a number of compelling applications. These techniques are not restricted to text analysis, however, and can be applied to other types of data which can be sensibly discretised and represented as counts of labels/properties/etc. LDA and it's variants have been used to find patterns in data from diverse areas of inquiry, including genetics, plant physiology, image analysis, social network analysis, remote sensing and astrophysics. Nonetheless, it is relatively recently that probabilistic topic models have found applications outside of text analysis, and to date few such applications have been considered. I suggest that there is substantial untapped potential for topic models and models inspired by or incorporating topic models to be fruitfully applied, and outline the characteristics of systems and data for which this may be the case.