Linganisha mbinu

	Usindikaji wa Lugha Asilia wa Multimodal	Uchanganuzi wa Hisia
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2021 (modern era, CLIP onward)	—
Mwanzilishi	Radford et al. (OpenAI) — CLIP, 2021; Li et al. — BLIP-2, 2023	—
Aina	Cross-modal understanding and generation pipeline	NLP text-classification task
Chanzo asilia	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.
Majina mbadala	Çok Kipli NLP (Multimodal NLP), vision-language models, multimodal learning	opinion mining, polarity detection, duygu analizi
Zinazohusiana	4	3
Muhtasari	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.