Võrdle meetodeid

	Tekstisageduse analüüs	TF-IDF	Teemamodelleerimine
Valdkond	Tekstikaeve	Tekstikaeve	Süvaõpe
Perekond	Process / pipeline	Process / pipeline	Machine learning
Tekkeaasta	1949	1988	1999–2003
Looja	George K. Zipf (frequency-distribution foundation)	Salton & Buckley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tüüp	Descriptive text-mining analysis	Text vectorization / term-weighting scheme	Unsupervised generative probabilistic model
Algallikas	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Rööpnimetused	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Seotud	4	3	5
Kokkuvõte	Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.