Linganisha mbinu

	Uchanganuzi wa Hisia Ulioboreshwa kwa Kikoa (Domain-adaptive Sentiment Analysis)	Mafunzo ya Uhamisho kwa Uainishaji unaotumia BERT
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2007	2019 (BERT); transfer learning paradigm established circa 2010
Mwanzilishi	Blitzer, J.; Dredze, M.; Pereira, F.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (BERT); Pan, S. J. & Yang, Q. (transfer learning survey)
Aina	Domain adaptation for text classification	Pre-trained transformer fine-tuned for classification
Chanzo asilia	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, 4171–4186. Association for Computational Linguistics.
Majina mbadala	cross-domain sentiment analysis, domain-adaptive opinion mining, domain transfer sentiment classification, DASA	BERT fine-tuning for classification, BERT transfer learning classifier, pre-trained BERT classifier, BERT downstream classification
Zinazohusiana	5	4
Muhtasari	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.	Transfer Learning with BERT-based Classification adapts a large transformer language model, pre-trained on massive text corpora, to a target classification task by fine-tuning its weights on labeled examples. The pre-trained representations encode rich syntactic and semantic knowledge, enabling high accuracy even when the labeled dataset is small.