Сравнение методов

	Адаптация к домену при анализе тональности	Многоязычный анализ тональности
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2007	2004–2020
Автор метода	Blitzer, J.; Dredze, M.; Pereira, F.	Pang, B. & Lee, L. (early sentiment analysis); cross-lingual extension via mBERT/XLM-R community (2019–2020)
Тип	Domain adaptation for text classification	Supervised classification / fine-tuned LM
Основополагающий источник	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.
Другие названия	cross-domain sentiment analysis, domain-adaptive opinion mining, domain transfer sentiment classification, DASA	cross-lingual sentiment analysis, multilingual opinion mining, multilingual sentiment classification, MSA
Связанные	5	5
Сводка	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.