Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Propagandas noteikšana× | Emociju noteikšana tekstā× | Analīze (Frame Analysis)× | |
|---|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | — | 1992 | 1982 |
| Autors≠ | — | Paul Ekman (basic-emotions theory) | Charles J. Fillmore |
| Tips≠ | NLP text-classification task | NLP text-classification task | NLP frame-semantic parsing task |
| Pirmavots≠ | Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP. DOI ↗ | Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200. DOI ↗ | Fillmore, C. J. (1982). Frame Semantics. In Linguistics in the Morning Calm. Seoul: Hanshin Publishing. ISBN: 9788970050355 |
| Citi nosaukumi≠ | propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti | emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection) | frame semantics, frame-semantic parsing, FrameNet analysis, Çerçeve Analizi (Frame Analysis) — NLP |
| Saistītās≠ | 4 | 3 | 4 |
| Kopsavilkums≠ | 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. | 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. |
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