手法を比較

	感情分析	転移学習
分野	テキストマイニング	機械学習
系統	Process / pipeline	Machine learning
提唱年	—	2010 (formalized); 1990s (early roots)
提唱者	—	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
種類	NLP text-classification task	Learning paradigm
原典	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.
別名	opinion mining, polarity detection, duygu analizi	TL, domain adaptation, fine-tuning, pre-trained model adaptation
関連	3	3
概要	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.