Linganisha mbinu

	Uchanganuzi wa Hisia	Uainishaji wa Maandishi	Kujifunza kwa uhamishaji
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Ujifunzaji wa Mashine
Familia	Process / pipeline	Process / pipeline	Machine learning
Mwaka wa asili	—	—	2010 (formalized); 1990s (early roots)
Mwanzilishi	—	—	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Aina	NLP text-classification task	Supervised NLP classification task	Learning paradigm
Chanzo asilia	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Majina mbadala	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Zinazohusiana	3	4	3
Muhtasari	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	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.