Comparar métodos

	Mineração de Argumentos	Análise de Sentimento	Classificação de Texto
Área	Mineração de texto	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	2016	—	—
Autor original	Lippi & Torroni (state-of-the-art survey)	—	—
Tipo	NLP information-extraction task	NLP text-classification task	Supervised NLP classification task
Fonte seminal	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Outros nomes	argumentation mining, argument extraction, Argüman Madenciliği	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Relacionados	4	3	4
Resumo	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.	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.