Comparar métodos

	Análisis de Coocurrencia	TF-IDF	Modelado de Temas
Campo	Minería de texto	Minería de texto	Aprendizaje profundo
Familia	Process / pipeline	Process / pipeline	Machine learning
Año de origen	1957	1988	1999–2003
Autor original	J.R. Firth (distributional principle)	Salton & Buckley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipo	Text-mining / distributional-semantics technique	Text vectorization / term-weighting scheme	Unsupervised generative probabilistic model
Fuente seminal	Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.	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.
Alias	word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Relacionados	4	3	5
Resumen	Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps.	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.