Comparer des méthodes

	Classification de texte	Modélisation par sujets
Domaine	Fouille de textes	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	—	1999–2003
Auteur d'origine	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Type	Supervised NLP classification task	Unsupervised generative probabilistic model
Source fondatrice	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	text categorization, document classification, topic classification, metin sınıflandırma	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Apparentées	4	5
Résumé	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	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.