Sammenlign metoder

	Propagandadeteksjon	Emosjonsdeteksjon i tekst	Tekstklassifisering
Fagfelt	Tekstutvinning	Tekstutvinning	Tekstutvinning
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Opprinnelsesår	—	1992	—
Opphavsperson	—	Paul Ekman (basic-emotions theory)	—
Type	NLP text-classification task	NLP text-classification task	Supervised NLP classification task
Opprinnelig kilde	Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP.	Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alias	propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti	emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection)	text categorization, document classification, topic classification, metin sınıflandırma
Relaterte	4	3	4
Sammendrag	Propaganda detection is a natural-language-processing task that automatically identifies and labels persuasion and manipulation techniques in text — such as loaded language, oversimplified solutions, bandwagon appeals, and glittering generalities. It builds on the fine-grained propaganda analysis introduced by Da San Martino et al. (2019), turning rhetorical manipulation into structured, technique-level labels.	Emotion detection is a natural-language-processing task that classifies the basic and complex emotions expressed in text — fear, joy, anger, sadness, surprise, and disgust — within a recognised emotion framework such as Ekman's basic-emotions model or Plutchik's wheel. It builds on Paul Ekman's 1992 argument for a small set of universal basic emotions, going beyond a simple positive/negative split to attach a specific emotion label to each piece of text.	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.