Bandingkan kaedah

	TF-IDF	Analisis Tematik	Pemodelan Topik
Bidang	Perlombongan Teks	Penyelidikan Kualitatif	Pembelajaran Mendalam
Keluarga	Process / pipeline	Process / pipeline	Machine learning
Tahun asal	1988	2006	1999–2003
Pengasas	Salton & Buckley	Virginia Braun and Victoria Clarke	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Jenis	Text vectorization / term-weighting scheme	Method	Unsupervised generative probabilistic model
Sumber perintis	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	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	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	TA, Reflexive Thematic Analysis	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Berkaitan	3	3	5
Ringkasan	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.	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.