Linganisha mbinu

	Uundaji mada wa NMF	Uchanganuzi wa Usomaji	TF-IDF
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1999	1975	1988
Mwanzilishi	Lee & Seung	J. Peter Kincaid et al.	Salton & Buckley
Aina	Matrix-factorization topic model	Text-mining readability scoring task	Text vectorization / term-weighting scheme
Chanzo asilia	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Kincaid, J.P., Fishburne, R.P., Rogers, R.L. & Chissom, B.S. (1975). Derivation of New Readability Formulas for Navy Enlisted Personnel. Naval Technical Training Command.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Majina mbadala	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	readability scoring, readability formulas, Flesch-Kincaid analysis, Okunabilirlik Analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Zinazohusiana	4	3	3
Muhtasari	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.	Readability analysis measures how well a text suits its intended audience by applying established readability formulas such as Flesch-Kincaid and Gunning Fog. The modern formula family was derived by Kincaid and colleagues in 1975, and it turns prose into a single score or target reading-grade level that signals how easy the text is to read.	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.