পদ্ধতির তুলনা করুন

	প্রশিক্ষণ ডেটা ছাড়াই টেক্সট ক্লাসিফিকেশন	অল্প-নমুনা পাঠ্য শ্রেণিবিভাগ	অনুভূতি বিশ্লেষণ
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2019	—	—
প্রবর্তক	Yin, Hay & Roth	—	—
ধরন	NLP text-classification task	NLP text-classification task (low-resource)	NLP text-classification task
মৌলিক উৎস	Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923.	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.
অপর নাম	zero-shot text classification, entailment-based classification, Sıfır Atışlı Sınıflandırma (Zero-Shot Classification)	few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot)	opinion mining, polarity detection, duygu analizi
সম্পর্কিত	3	4	3
সারসংক্ষেপ	Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets.	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.