Salīdzināt metodes

	Domēnam adaptīva sentimenta analīze	BERT klasifikācija
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2007	2019
Autors	Blitzer, J.; Dredze, M.; Pereira, F.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Tips	Domain adaptation for text classification	Pre-trained language model with fine-tuning
Pirmavots	Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), 440–447.	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.
Citi nosaukumi	cross-domain sentiment analysis, domain-adaptive opinion mining, domain transfer sentiment classification, DASA	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Saistītās	5	4
Kopsavilkums	Domain-adaptive sentiment analysis trains a sentiment model on one or more labeled source domains (e.g., product reviews) and adapts it to a target domain (e.g., social media posts or news) where labels are scarce or absent. By bridging the vocabulary and distributional gap between domains, it achieves strong sentiment classification without requiring large labeled corpora in every target domain.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.