Krahasoni metodat

	Vlerësimi Automatik i Tekstit	Analiza e ndjenjave
Fusha	Nxjerrja e tekstit	Nxjerrja e tekstit
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)	—
Krijuesi	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)	—
Lloji	Reference-based NLG evaluation metric suite	NLP text-classification task
Burimi themelues	Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Emërtime të tjera	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics	opinion mining, polarity detection, duygu analizi
Të lidhura	4	3
Përmbledhja	Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generation (NLG) outputs — by comparing them to one or more human-written reference texts. Pioneered by Papineni et al. with BLEU in 2002, the field has grown to include n-gram overlap metrics (BLEU, ROUGE) and semantically aware metrics (BERTScore, MoverScore) that capture meaning beyond surface word matches.	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.