Compară metode

	Evaluarea automată a textului	Analiza sentimentelor	Modelarea tematică
Domeniu	Mineritul textelor	Mineritul textelor	Învățare profundă
Familie	Process / pipeline	Process / pipeline	Machine learning
Anul apariției	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)	—	1999–2003
Autorul original	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tip	Reference-based NLG evaluation metric suite	NLP text-classification task	Unsupervised generative probabilistic model
Sursa seminală	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Denumiri alternative	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics	opinion mining, polarity detection, duygu analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Înrudite	4	3	5
Rezumat	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.