Compara mètodes

	Anàlisi de freqüència de text	Diversitat lèxica	TF-IDF
Camp	Mineria de text	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	1949	—	1988
Autor original	George K. Zipf (frequency-distribution foundation)	—	Salton & Buckley
Tipus	Descriptive text-mining analysis	Text quantification / lexical richness measurement	Text vectorization / term-weighting scheme
Font seminal	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.	McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Àlies	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relacionats	4	3	3
Resum	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.	Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures.	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.