Machine learningDeep learning / NLP / CV
可解释情感分析
可解释情感分析将情感分类模型（通常是经过微调的BERT或RoBERTa等Transformer模型）与事后或内在解释方法（SHAP、LIME、注意力可视化或集成梯度）相结合，以揭示是哪些词语、短语或特征驱动了每次预测。其目标是实现高预测准确性，并为每个标签提供透明、可审计的理由。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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. link ↗
- Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Sentiment Analysis (XAI-augmented Opinion Mining). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-sentiment-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- [需翻译标题：BERT-based Classification...]深度学习↔ compare
- 可解释的BERT分类深度学习↔ compare
- 基于RoBERTa的分类深度学习↔ compare
- 句子嵌入深度学习↔ compare
- 主题建模深度学习↔ compare