Topic subject creation using unsupervised learning for topic modeling
This work addresses topic modeling for retail customer service, but it appears incremental as it compares existing methods without introducing a new paradigm.
The paper tackled the problem of automatically characterizing customer inquiry subjects in retail communications by comparing Non-Negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) for topic mining and proposing automated labeling methods, but no concrete performance numbers or results were provided.
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.