قارن الطرق

	تحليل المشاعر المستند إلى الجوانب (ABSA)	نمذجة الموضوعات
المجال	تنقيب النصوص	التعلم العميق
العائلة	Process / pipeline	Machine learning
سنة النشأة	2014	1999–2003
صاحب الطريقة	Pontiki et al. (SemEval-2014 Task 4)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
النوع	NLP fine-grained opinion-mining task	Unsupervised generative probabilistic model
المصدر التأسيسي	Pontiki, M. et al. (2014). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of SemEval 2014, 27-35.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
الأسماء البديلة	ABSA, aspect-level sentiment analysis, feature-based sentiment analysis, Konu Bazlı Duygu Analizi (ABSA)	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
ذات صلة	4	5
الملخص	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.	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.