手法を比較

	トピックモデリング	文書クラスタリング	TF-IDF	Word2Vec
分野	テキストマイニング	テキストマイニング	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	2003	—	1988	2013
提唱者	Blei, Ng & Jordan	—	Salton & Buckley	Tomas Mikolov et al.
種類	Generative probabilistic topic model	Unsupervised text-mining task	Text vectorization / term-weighting scheme	Neural word-embedding model
原典	Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
別名	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
関連	4	4	3	4
概要	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.	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	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.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.