So sánh phương pháp

	Phân tích Cảm xúc Bán Giám sát	Phân tích tình cảm tự giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2002–2008	2019–present
Người khởi xướng	Zhu, X.; Pang, B. & Lee, L. (foundational works)	Devlin et al. (BERT paradigm); extended by Sun et al. and others
Loại	Semi-supervised classification	Pre-train then fine-tune NLP pipeline
Công trình gốc	Zhu, X. (2005). Semi-Supervised Learning Literature Survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.	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.
Tên gọi khác	SSSA, semi-supervised opinion mining, label-propagation sentiment classification, self-training sentiment analysis	SSL-based sentiment analysis, self-supervised opinion mining, pre-training for sentiment, unsupervised pre-training sentiment
Liên quan	4	2
Tóm tắt	Semi-supervised sentiment analysis combines a small set of manually labeled text samples with a large pool of unlabeled text to train opinion classifiers. By propagating sentiment signals from labeled seeds to unlabeled data through self-training, label propagation, or consistency regularization, the approach achieves competitive accuracy without the cost of labeling large corpora.	Self-supervised sentiment analysis combines large-scale unsupervised pre-training — through objectives such as masked language modeling or contrastive prediction — with fine-tuning on a small labeled sentiment corpus. The approach, popularized by BERT and its variants, dramatically reduces the need for hand-labeled data while achieving state-of-the-art accuracy on positive/negative/neutral opinion classification tasks.