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| Analiza sentimenta× | Vision Transformer× | |
|---|---|---|
| Oblast≠ | Rudarenje teksta | Duboko učenje |
| Porodica≠ | Process / pipeline | Machine learning |
| Godina nastanka≠ | — | 2021 |
| Tvorac≠ | — | Dosovitskiy, A. et al. |
| Tip≠ | NLP text-classification task | Transformer architecture for images (self-attention over patches) |
| Temeljni izvor≠ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Drugi nazivi≠ | opinion mining, polarity detection, duygu analizi | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Srodne≠ | 3 | 5 |
| Sažetak≠ | 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). |
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