Salīdzināt metodes

	Domēnam adaptīva sentimenta analīze	Daudzvalodu noskaņojuma analīze
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2007	2004–2020
Autors	Blitzer, J.; Dredze, M.; Pereira, F.	Pang, B. & Lee, L. (early sentiment analysis); cross-lingual extension via mBERT/XLM-R community (2019–2020)
Tips	Domain adaptation for text classification	Supervised classification / fine-tuned LM
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.	Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzman, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL 2020, 8440–8451.
Citi nosaukumi	cross-domain sentiment analysis, domain-adaptive opinion mining, domain transfer sentiment classification, DASA	cross-lingual sentiment analysis, multilingual opinion mining, multilingual sentiment classification, MSA
Saistītās	5	5
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.	Multilingual Sentiment Analysis (MSA) applies deep learning — most commonly a fine-tuned multilingual language model such as mBERT or XLM-RoBERTa — to classify the sentiment polarity (positive, negative, neutral) of text written in two or more languages, enabling opinion mining across language boundaries without building separate models per language.