Porównaj metody

	Modelowanie tematyczne	TF-IDF
Dziedzina	Eksploracja tekstu	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2003	1988
Twórca	Blei, Ng & Jordan	Salton & Buckley
Typ	Generative probabilistic topic model	Text vectorization / term-weighting scheme
Źródło pierwotne	Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Inne nazwy	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Pokrewne	4	3
Podsumowanie	Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.