Võrdle meetodeid

	Arvamuste kaevandamine	Argumentide kaevandamine	Teksti klassifitseerimine
Valdkond	Tekstikaeve	Tekstikaeve	Tekstikaeve
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	2012	2016	—
Looja	Bing Liu	Lippi & Torroni (state-of-the-art survey)	—
Tüüp	NLP information-extraction task	NLP information-extraction task	Supervised NLP classification task
Algallikas	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Rööpnimetused	aspect-based sentiment analysis, opinion extraction, Görüş Madenciliği (Opinion Mining)	argumentation mining, argument extraction, Argüman Madenciliği	text categorization, document classification, topic classification, metin sınıflandırma
Seotud	3	4	4
Kokkuvõte	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.	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.