Methoden vergelijken

	Documentclustering	Sleutelwoordextractie	Thematische Analyse	Onderwerpmodellering
Vakgebied	Tekstmining	Tekstmining	Kwalitatief onderzoek	Deep learning
Familie	Process / pipeline	Process / pipeline	Process / pipeline	Machine learning
Jaar van ontstaan	—	—	2006	1999–2003
Grondlegger	—	—	Virginia Braun and Victoria Clarke	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Type	Unsupervised text-mining task	NLP text-mining task	Method	Unsupervised generative probabilistic model
Oorspronkelijke bron	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	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.
Aliassen	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	TA, Reflexive Thematic Analysis	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Verwant	4	4	3	5
Samenvatting	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).	Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).	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.