Bandingkan metode

	Analisis Ko-okurensi	Pemodelan Topik
Bidang	Penambangan Teks	Pembelajaran Mendalam
Keluarga	Process / pipeline	Machine learning
Tahun asal	1957	1999–2003
Pencetus	J.R. Firth (distributional principle)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipe	Text-mining / distributional-semantics technique	Unsupervised generative probabilistic model
Sumber perintis	Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.	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	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Terkait	4	5
Ringkasan	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.	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.