Σύγκριση μεθόδων

	Ανίχνευση Υποκειμενικότητας	Ανίχνευση Συναισθήματος σε Κείμενο
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	—	1992
Δημιουργός	—	Paul Ekman (basic-emotions theory)
Τύπος	NLP text-classification task	NLP text-classification task
Θεμελιώδης πηγή	Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210.	Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200.
Εναλλακτικές ονομασίες	subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection)	emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection)
Συναφείς	3	3
Σύνοψη	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.