Comparar métodos

	Modelagem de Tópicos por Fatoração de Matriz Não-Negativa (NMF)	TF-IDF
Área	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline
Ano de origem	1999	1988
Autor original	Lee & Seung	Salton & Buckley
Tipo	Matrix-factorization topic model	Text vectorization / term-weighting scheme
Fonte seminal	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Outros nomes	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relacionados	4	3
Resumo	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.	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.