Methoden vergleichen

	Automatisierte Aufsatzbewertung (AES)	Sentiment-Analyse
Fachgebiet	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1966 (Project Essay Grade); modern deep-learning era from 2019	—
Urheber	Shermis & Burstein (eds.); landmark consolidation 2013; deep-learning era from Devlin et al. 2019	—
Typ	Supervised text-regression / text-classification task	NLP text-classification task
Wegweisende Quelle	Shermis, M.D. & Burstein, J. (2013). Handbook of Automated Essay Evaluation. Routledge.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Aliasnamen	AES, automated writing evaluation, AWE, Otomatik Deneme Puanlaması	opinion mining, polarity detection, duygu analizi
Verwandt	4	3
Zusammenfassung	Automated Essay Scoring (AES) is a natural-language-processing task in which a computational model assigns scores to student-written essays across dimensions such as grammatical correctness, coherence, content richness, and organisation — replicating, at scale, what a human rater would do. The approach was formalised as a research field by Shermis and Burstein (2013) and has been transformed since 2019 by transformer language models, particularly BERT, which allow AES systems to leverage deep contextual representations of text.	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.