手法を比較

	テキスト頻度分析	トピックモデリング
分野	テキストマイニング	深層学習
系統	Process / pipeline	Machine learning
提唱年	1949	1999–2003
提唱者	George K. Zipf (frequency-distribution foundation)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
種類	Descriptive text-mining analysis	Unsupervised generative probabilistic model
原典	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
別名	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
関連	4	5
概要	Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.	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.