Confronta i metodi

	Clustering di documenti	Analisi Tematica	Topic Modeling
Campo	Text mining	Ricerca qualitativa	Apprendimento profondo
Famiglia	Process / pipeline	Process / pipeline	Machine learning
Anno di origine	—	2006	1999–2003
Ideatore	—	Virginia Braun and Victoria Clarke	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipo	Unsupervised text-mining task	Method	Unsupervised generative probabilistic model
Fonte seminale	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	TA, Reflexive Thematic Analysis	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Correlati	4	3	5
Sintesi	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).	Thematic Analysis (TA) is a qualitative research methodology for identifying, analyzing, and reporting patterns (themes) in qualitative data. Developed systematically by Virginia Braun and Victoria Clarke (2006), TA is flexible and accessible, applicable across diverse theoretical frameworks and data types, making it one of the most widely used qualitative methods in psychology, health research, and social sciences.	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.