Machine learningDeep learning / NLP / CV
自监督情感分析
自监督情感分析结合了大规模无监督预训练——通过掩码语言模型或对比预测等目标——与在小型标记情感语料库上的微调。这种方法由BERT及其变体推广，极大地减少了手工标记数据的需求，同时在正面/负面/中性意见分类任务上实现了最先进的准确性。
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来源
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics. DOI: 10.18653/v1/N19-1423 ↗
- Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to fine-tune BERT for text classification? In China National Conference on Chinese Computational Linguistics (CCL 2019), pp. 194–206. Springer. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Learning for Sentiment Analysis. ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-sentiment-analysis
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