Comparer des méthodes

	Analyse des sentiments basée sur les aspects (ABSA)	Classification de texte
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	2014	—
Auteur d'origine	Pontiki et al. (SemEval-2014 Task 4)	—
Type	NLP fine-grained opinion-mining task	Supervised NLP classification task
Source fondatrice	Pontiki, M. et al. (2014). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of SemEval 2014, 27-35.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alias	ABSA, aspect-level sentiment analysis, feature-based sentiment analysis, Konu Bazlı Duygu Analizi (ABSA)	text categorization, document classification, topic classification, metin sınıflandırma
Apparentées	4	4
Résumé	Aspect-based sentiment analysis (ABSA) is a fine-grained natural-language-processing task that detects sentiment separately for each aspect or feature mentioned in a text — such as a product's quality, price, or service — rather than scoring the document as a whole. It was consolidated as a shared task by Pontiki et al. in SemEval-2014 Task 4.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.