Porównaj metody

	Analiza współwystępowania	Ekstrakcja słów kluczowych	TF-IDF	Modelowanie tematów
Dziedzina	Eksploracja tekstu	Eksploracja tekstu	Eksploracja tekstu	Uczenie głębokie
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline	Machine learning
Rok powstania	1957	—	1988	1999–2003
Twórca	J.R. Firth (distributional principle)	—	Salton & Buckley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Typ	Text-mining / distributional-semantics technique	NLP text-mining task	Text vectorization / term-weighting scheme	Unsupervised generative probabilistic model
Źródło pierwotne	Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	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.
Inne nazwy	word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Pokrewne	4	4	3	5
Podsumowanie	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.	Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).	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.