School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Document Type

Article

Publication Date

3-27-2023

Abstract

We propose a multi-scale hybridized topic modeling method to find hidden topics from transcribed interviews more accurately and more efficiently than traditional topic modeling methods. Our multiscale hybridized topic modeling method (MSHTM) approaches data at different scales and performs topic modeling in a hierarchical way utilizing first a classical method, Nonnegative Matrix Factorization, and then a transformer-based method, BERTopic. It harnesses the strengths of both NMF and BERTopic. Our method can help researchers and the public better extract and interpret the interview information. Additionally, it provides insights for new indexing systems based on the topic level. We then deploy our method on real-world interview transcripts and find promising results.

Comments

Original published version available at https://doi.org/10.1137/22S1536832

Publication Title

SIAM Undergraduate Research Online 16

DOI

10.1137/22S1536832

Included in

Mathematics Commons

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