Porovnať metódy

	NMF modelovanie tém	Analýza sentimentu	TF-IDF
Odbor	Dolovanie textu	Dolovanie textu	Dolovanie textu
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1999	—	1988
Tvorca	Lee & Seung	—	Salton & Buckley
Typ	Matrix-factorization topic model	NLP text-classification task	Text vectorization / term-weighting scheme
Pôvodný zdroj	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Ďalšie názvy	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Príbuzné	4	3	3
Zhrnutie	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.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.	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.