مقایسهٔ روش‌ها

	مدل‌سازی موضوعی تبیین‌پذیر	مدل موضوعی NMF
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2003–2020s	1999
پدیدآور	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Lee, D. D. & Seung, H. S.
نوع	Unsupervised topic discovery + interpretability layer	Matrix factorization / unsupervised topic model
منبع بنیادین	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
نام‌های دیگر	XTM, interpretable topic modeling, transparent topic modeling, explainable LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
مرتبط	6	4
خلاصه	Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team.	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.