Bandingkan kaedah

	Pemodelan Topik NMF	Analisis Kebolehbacaan
Bidang	Perlombongan Teks	Perlombongan Teks
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1999	1975
Pengasas	Lee & Seung	J. Peter Kincaid et al.
Jenis	Matrix-factorization topic model	Text-mining readability scoring task
Sumber perintis	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.
Alias	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	readability scoring, readability formulas, Flesch-Kincaid analysis, Okunabilirlik Analizi
Berkaitan	4	3
Ringkasan	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.