LDA is a generative probabilistic model for discrete datasets. It is a three-level hierarchical Bayesian model, where each collection item is represented as a finite mixture over an underlying set of topics, and each topic is represented as an infinite mixture over a collection of topic probabilities. Since the number of topics need not be predefined, LDA provides researchers with an efficient resource to obtain an explicit representation of a document. In contrast to LDA, NMF is a decompositional, non-probabilistic algorithm using matrix factorization and belongs to the group of linear-algebraic algorithms. NMF works on TF-IDF-transformed data by breaking down a matrix into two lower-ranking matrices. Specifically, NMF decomposes its input, which is a term-document matrix (A), into a product of a terms-topics matrix (W) and a topics-documents matrix (H). W contains the basis vectors, and H contains the corresponding weights.

Egger, R., & Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in sociology, 7, 886498.

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic. URL: https://www.datascience-in-tourism.com/?p=550 (accessed: 29 October 2024).