Compară metode

	Detecția emoțiilor în text	Analiza cadrelor	Analiza sentimentelor
Domeniu	Mineritul textelor	Mineritul textelor	Mineritul textelor
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Anul apariției	1992	1982	—
Autorul original	Paul Ekman (basic-emotions theory)	Charles J. Fillmore	—
Tip	NLP text-classification task	NLP frame-semantic parsing task	NLP text-classification task
Sursa seminală	Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200.	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.
Denumiri alternative	emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection)	frame semantics, frame-semantic parsing, FrameNet analysis, Çerçeve Analizi (Frame Analysis) — NLP	opinion mining, polarity detection, duygu analizi
Înrudite	3	4	3
Rezumat	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.	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.