Võrdle meetodeid

	NMF-i teemamodelleerimine	Sentimentanalüüs
Valdkond	Tekstikaeve	Tekstikaeve
Perekond	Process / pipeline	Process / pipeline
Tekkeaasta	1999	—
Looja	Lee & Seung	—
Tüüp	Matrix-factorization topic model	NLP text-classification task
Algallikas	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.
Rööpnimetused	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	opinion mining, polarity detection, duygu analizi
Seotud	4	3
Kokkuvõte	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.