Compara mètodes

	Detecció de propaganda	Anàlisi de marcs	Anàlisi de sentiments
Camp	Mineria de text	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	—	1982	—
Autor original	—	Charles J. Fillmore	—
Tipus	NLP text-classification task	NLP frame-semantic parsing task	NLP text-classification task
Font seminal	Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP.	Fillmore, C. J. (1982). Frame Semantics. In Linguistics in the Morning Calm. Seoul: Hanshin Publishing. ISBN: 9788970050355	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Àlies	propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti	frame semantics, frame-semantic parsing, FrameNet analysis, Çerçeve Analizi (Frame Analysis) — NLP	opinion mining, polarity detection, duygu analizi
Relacionats	4	4	3
Resum	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.	Frame analysis is a FrameNet-based natural-language-processing task that detects the semantic frames evoked in text and the participant roles (frame-evoking elements and frame elements, FE) that fill them. Rooted in Charles Fillmore's frame semantics (1982) and operationalised by the Berkeley FrameNet Project (Baker et al., 1998), it is widely used to analyse media discourse and political 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.