Comparer des méthodes

	Analyse des sentiments	Apprentissage par transfert
Domaine	Fouille de textes	Apprentissage automatique
Famille	Process / pipeline	Machine learning
Année d'origine	—	2010 (formalized); 1990s (early roots)
Auteur d'origine	—	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	NLP text-classification task	Learning paradigm
Source fondatrice	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.
Alias	opinion mining, polarity detection, duygu analizi	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Apparentées	3	3
Résumé	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.