Topic subject creation using unsupervised learning for topic modeling

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 t...

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Bibliographic Details
Main Authors Mehdiyev, Rashid, Nava, Jean, Sodhi, Karan, Acharya, Saurav, Rana, Annie Ibrahim
Format Journal Article
LanguageEnglish
Published 18.12.2019
Subjects
Computer Science - Computation and Language
Computer Science - Computers and Society
Computer Science - Information Retrieval
Computer Science - Learning
Statistics - Machine Learning
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DOI10.48550/arxiv.1912.08868

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Summary: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.
DOI:10.48550/arxiv.1912.08868