Compara mètodes

	PNL Multimodal	Anàlisi de sentiments	Vision Transformer
Camp	Mineria de text	Mineria de text	Aprenentatge profund
Família	Process / pipeline	Process / pipeline	Machine learning
Any d'origen	2021 (modern era, CLIP onward)	—	2021
Autor original	Radford et al. (OpenAI) — CLIP, 2021; Li et al. — BLIP-2, 2023	—	Dosovitskiy, A. et al.
Tipus	Cross-modal understanding and generation pipeline	NLP text-classification task	Transformer architecture for images (self-attention over patches)
Font seminal	Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), 8748–8763.	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.
Àlies	Çok Kipli NLP (Multimodal NLP), vision-language models, multimodal learning	opinion mining, polarity detection, duygu analizi	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Relacionats	4	3	5
Resum	Multimodal NLP is a family of natural-language-processing pipelines that combine text with one or more additional data modalities — most commonly images, but also audio and video — to perform understanding and generation tasks such as visual question answering, image captioning, and multimodal sentiment recognition. The field gained its modern form with CLIP (Radford et al., 2021) and has since advanced through architectures such as BLIP-2 (Li et al., 2023) that bridge frozen image encoders and large language models.	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).