Bandingkan kaedah

	Pemodelan Topik Boleh Dijelaskan	Klasifikasi Berasaskan BERT yang Boleh Dijelaskan
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2003–2020s	2019–2020
Pengasas	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Jenis	Unsupervised topic discovery + interpretability layer	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Sumber perintis	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.
Alias	XTM, interpretable topic modeling, transparent topic modeling, explainable LDA	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Berkaitan	6	6
Ringkasan	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.	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.