So sánh phương pháp

	Phân tích Cảm xúc Có thể Giải thích	Phân loại dựa trên BERT có thể giải thích
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2016–2020	2019–2020
Người khởi xướng	Multiple contributors (LIME: Ribeiro et al. 2016; SHAP: Lundberg & Lee 2017; attention-based XAI in NLP: numerous, 2018–2020)	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Loại	Interpretable NLP pipeline	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Công trình gốc	Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the ACL and the 10th IJCNLP, 447–459.	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.
Tên gọi khác	XAI sentiment analysis, interpretable sentiment classification, transparent opinion mining, explainable opinion analysis	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Liên quan	5	6
Tóm tắt	Explainable sentiment analysis pairs a sentiment classification model — typically a fine-tuned transformer such as BERT or RoBERTa — with a post-hoc or intrinsic explanation method (SHAP, LIME, attention visualization, or integrated gradients) that reveals which words, phrases, or features drove each prediction. The goal is both high predictive accuracy and transparent, auditable rationales for every label.	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.