Linganisha mbinu

	Uchanganuzi wa Propaganda	Uchanganuzi wa Hisia katika Maandishi	Uchanganuzi wa Hisia
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	—	1992	—
Mwanzilishi	—	Paul Ekman (basic-emotions theory)	—
Aina	NLP text-classification task	NLP text-classification task	NLP text-classification task
Chanzo asilia	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.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Majina mbadala	propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti	emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection)	opinion mining, polarity detection, duygu analizi
Zinazohusiana	4	3	3
Muhtasari	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.	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.