مقایسهٔ روش‌ها

	تحلیل احساسات	طبقه‌بندی متن	یادگیری انتقالی
حوزه	متن‌کاوی	متن‌کاوی	یادگیری ماشین
خانواده	Process / pipeline	Process / pipeline	Machine learning
سال پیدایش	—	—	2010 (formalized); 1990s (early roots)
پدیدآور	—	—	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
نوع	NLP text-classification task	Supervised NLP classification task	Learning paradigm
منبع بنیادین	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.
نام‌های دیگر	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma	TL, domain adaptation, fine-tuning, pre-trained model adaptation
مرتبط	3	4	3
خلاصه	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.