Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Hisia katika Maandishi× | Uchanganuzi wa Hisia× | |
|---|---|---|
| Nyanja | Uchimbaji wa Matini | Uchimbaji wa Matini |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1992 | — |
| Mwanzilishi≠ | Paul Ekman (basic-emotions theory) | — |
| Aina | NLP text-classification task | NLP text-classification task |
| Chanzo asilia≠ | Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Majina mbadala | emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection) | opinion mining, polarity detection, duygu analizi |
| Zinazohusiana | 3 | 3 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
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