Сравнение методов

	Адаптация домена	Анализ тональности	Перенос обучения
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста	Машинное обучение
Семейство	Process / pipeline	Process / pipeline	Machine learning
Год появления	—	—	2010 (formalized); 1990s (early roots)
Автор метода	—	—	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	NLP transfer-learning / fine-tuning pipeline	NLP text-classification task	Learning paradigm
Основополагающий источник	Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning	opinion mining, polarity detection, duygu analizi	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	4	3	3
Сводка	Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.	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.