Сравнение методов

	Обнаружение пропаганды	Детекция эмоций в тексте
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline
Год появления	—	1992
Автор метода	—	Paul Ekman (basic-emotions theory)
Тип	NLP text-classification task	NLP text-classification task
Основополагающий источник	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.
Другие названия	propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti	emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection)
Связанные	4	3
Сводка	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.