  We describe the use of Non-Negative Matrix Factorization (NMF) and Latent
Dirichlet Allocation (LDA) algorithms to perform topic mining and labelling
applied to retail customer communications in attempt to characterize the
subject of customers inquiries. In this paper we compare both algorithms in the
topic mining performance and propose methods to assign topic subject labels in
an automated way.
