So sánh phương pháp

	Phân loại văn bản với số lượng ví dụ ít (Few-Shot Text Classification)	Phân tích Cảm xúc
Lĩnh vực	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline
Năm ra đời	—	—
Người khởi xướng	—	—
Loại	NLP text-classification task (low-resource)	NLP text-classification task
Công trình gốc	Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Tên gọi khác	few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot)	opinion mining, polarity detection, duygu analizi
Liên quan	4	3
Tóm tắt	Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels.	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.