Sammenlign metoder

	Emotionsdetektion i tekst	Tekstklassificering
Fagområde	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1992	—
Ophavsperson	Paul Ekman (basic-emotions theory)	—
Type	NLP text-classification task	Supervised NLP classification task
Oprindelig kilde	Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliasser	emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection)	text categorization, document classification, topic classification, metin sınıflandırma
Relaterede	3	4
Resumé	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.