Porovnat metody

	Detekce záměru	Analýza sentimentu	Klasifikace textu
Obor	Dolování textu	Dolování textu	Dolování textu
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	—	—	—
Tvůrce	—	—	—
Typ	NLP / NLU text-classification task	NLP text-classification task	Supervised NLP classification task
Původní zdroj	Larson, S. et al. (2019). An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction. EMNLP.	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.
Další názvy	intent classification, intent recognition, Niyet Tespiti (Intent Detection)	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Příbuzné	4	3	4
Shrnutí	Intent detection is a natural-language-understanding task that classifies the purpose behind a user utterance — such as making a reservation, asking for information, or filing a complaint — into one of a set of predefined intent classes. It is a core NLU component of conversational interfaces and customer-service automation systems, drawing on the benchmarks of Larson et al. (2019) and Casanueva et al. (2020).	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.