Compară metode

	Răspunsul la întrebări (QA)	Analiza sentimentelor
Domeniu	Mineritul textelor	Mineritul textelor
Familie	Process / pipeline	Process / pipeline
Anul apariției	—	—
Autorul original	—	—
Tip	NLP text-comprehension task	NLP text-classification task
Sursa seminală	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Denumiri alternative	QA, machine reading comprehension, Soru Cevaplama (Question Answering)	opinion mining, polarity detection, duygu analizi
Înrudite	4	3
Rezumat	Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.	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.