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| Rilevamento della Soggettività× | Rilevamento delle emozioni nel testo× | |
|---|---|---|
| Campo | Text mining | Text mining |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | — | 1992 |
| Ideatore≠ | — | Paul Ekman (basic-emotions theory) |
| Tipo | NLP text-classification task | NLP text-classification task |
| Fonte seminale≠ | Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210. DOI ↗ | Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200. DOI ↗ |
| Alias | subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection) | emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection) |
| Correlati | 3 | 3 |
| Sintesi≠ | Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis. | 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. |
| ScholarGateInsieme di dati ↗ |
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