เปรียบเทียบวิธี

	Explainable NMF Topic Model	Explainable LDA Topic Model
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2001 (NMF); XAI integration ~2017–present	2003 (LDA); 2018–present (explainability extensions)
ผู้ริเริ่ม	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors
ประเภท	Interpretable unsupervised topic model	Probabilistic generative topic model with interpretability enhancements
แหล่งต้นตำรับ	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
ชื่อเรียกอื่น	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model
ที่เกี่ยวข้อง	6	4
สรุป	An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human readers.	Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery.