方法对比

	主题建模	TF-IDF
领域	文本挖掘	文本挖掘
方法族	Process / pipeline	Process / pipeline
起源年份	2003	1988
提出者	Blei, Ng & Jordan	Salton & Buckley
类型	Generative probabilistic topic model	Text vectorization / term-weighting scheme
开创性文献	Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
别名	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
相关	4	3
摘要	Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.	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.