手法を比較

	NMFトピックモデリング	BERTopic
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	1999	2022
提唱者	Lee & Seung	Maarten Grootendorst
種類	Matrix-factorization topic model	Neural topic-modeling pipeline
原典	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794.
別名	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	neural topic modeling, transformer topic modeling, Konu Modelleme — BERTopic
関連	4	3
概要	NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.	BERTopic is a neural topic-modeling pipeline introduced by Maarten Grootendorst in 2022. It combines BERT-based contextual embeddings with UMAP dimensionality reduction and HDBSCAN clustering to produce coherent, dynamic topics, achieving higher topic coherence than classic topic models.