Comparar métodos

	Classificação de Texto	Análise de Sentimento
Área	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline
Ano de origem	—	—
Autor original	—	—
Tipo	Supervised NLP classification task	NLP text-classification task
Fonte seminal	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Outros nomes	text categorization, document classification, topic classification, metin sınıflandırma	opinion mining, polarity detection, duygu analizi
Relacionados	4	3
Resumo	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.	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.