Comparer des méthodes

	Analyse des sentiments	Vision Transformer
Domaine	Fouille de textes	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	—	2021
Auteur d'origine	—	Dosovitskiy, A. et al.
Type	NLP text-classification task	Transformer architecture for images (self-attention over patches)
Source fondatrice	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Alias	opinion mining, polarity detection, duygu analizi	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Apparentées	3	5
Résumé	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.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).