Usporedite metode

	Modeliranje tema	TF-IDF
Područje	Rudarenje teksta	Rudarenje teksta
Obitelj	Process / pipeline	Process / pipeline
Godina nastanka	2003	1988
Tvorac	Blei, Ng & Jordan	Salton & Buckley
Vrsta	Generative probabilistic topic model	Text vectorization / term-weighting scheme
Temeljni izvor	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.
Drugi nazivi	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Srodne	4	3
Sažetak	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.