Сравнение на методи

	Домейнна адаптация	Анализ на настроенията
Област	Извличане на текст	Извличане на текст
Семейство	Process / pipeline	Process / pipeline
Година на възникване	—	—
Създател	—	—
Тип	NLP transfer-learning / fine-tuning pipeline	NLP text-classification task
Основополагащ източник	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.
Други названия	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning	opinion mining, polarity detection, duygu analizi
Свързани	4	3
Резюме	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.