השוואת שיטות

	ניתוח סנטימנט מבוסס-היבטים (ABSA)	סיווג טקסט
תחום	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2014	—
הוגה השיטה	Pontiki et al. (SemEval-2014 Task 4)	—
סוג	NLP fine-grained opinion-mining task	Supervised NLP classification task
מקור מכונן	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.
כינויים	ABSA, aspect-level sentiment analysis, feature-based sentiment analysis, Konu Bazlı Duygu Analizi (ABSA)	text categorization, document classification, topic classification, metin sınıflandırma
קשורות	4	4
תקציר	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.