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.
@article{arxiv.1912.08868,
title = {Topic subject creation using unsupervised learning for topic modeling},
author = {Rashid Mehdiyev and Jean Nava and Karan Sodhi and Saurav Acharya and Annie Ibrahim Rana},
journal= {arXiv preprint arXiv:1912.08868},
year = {2019}
}