Salīdzināt metodes

	Pašuzraudzītā sentimenta analīze	Pārneses apmācība
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–present	2010 (formalized); 1990s (early roots)
Autors	Devlin et al. (BERT paradigm); extended by Sun et al. and others	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tips	Pre-train then fine-tune NLP pipeline	Learning paradigm
Pirmavots	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Citi nosaukumi	SSL-based sentiment analysis, self-supervised opinion mining, pre-training for sentiment, unsupervised pre-training sentiment	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Saistītās	2	3
Kopsavilkums	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.