Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets using Topic Modeling

arXiv preprint, 2022

Recommended citation: Cheng, K; Inzer, S.; Leung, A.; Shen, X.; Perlmutter, M.; Lindstrom, M.; Chew, J.A.; Presner, T.; Needell, D. Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets using Topic Modeling. Submitted for publication, 2022. Online access

We propose a multi-scale hybridized topic modeling method to find hidden topics from transcribed interviews more accurately and efficiently than traditional topic modeling methods. Our multi-scale 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.