Bandingkan kaedah

	Analisis Sentimen Semi-Terawasi	Klasifikasi Berasaskan BERT Separuh-Terawasi
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2002–2008	2019–2020
Pengasas	Zhu, X.; Pang, B. & Lee, L. (foundational works)	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)
Jenis	Semi-supervised classification	Semi-supervised fine-tuning of pre-trained transformer
Sumber perintis	Zhu, X. (2005). Semi-Supervised Learning Literature Survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.	Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.
Alias	SSSA, semi-supervised opinion mining, label-propagation sentiment classification, self-training sentiment analysis	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning
Berkaitan	4	6
Ringkasan	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.	Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.