Comparer des méthodes

	Exploitation des opinions	Extraction d'arguments	Analyse des sentiments
Domaine	Fouille de textes	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	2012	2016	—
Auteur d'origine	Bing Liu	Lippi & Torroni (state-of-the-art survey)	—
Type	NLP information-extraction task	NLP information-extraction task	NLP text-classification task
Source fondatrice	Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool.	Lippi, M. & Torroni, P. (2016). Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 16(2), Article 10, 1-25.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Alias	aspect-based sentiment analysis, opinion extraction, Görüş Madenciliği (Opinion Mining)	argumentation mining, argument extraction, Argüman Madenciliği	opinion mining, polarity detection, duygu analizi
Apparentées	3	4	3
Résumé	Opinion mining is a natural-language-processing task that systematically extracts and analyses user opinions about a product, service, or topic — identifying the specific features (aspects) being discussed, the sentiment expressed toward each, and the opinion holders. Consolidated by Bing Liu (2012), it goes beyond a single document-level label to produce structured aspect–opinion–holder records.	Argument mining is a natural-language-processing task that automatically detects claims, premises and the argumentative structures that link them within text. Consolidated as a field by Lippi and Torroni's 2016 state-of-the-art survey, it is applied to scientific writing, legal documents and debate analysis to turn free-form argumentation into structured, analysable units.	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.