Applying textmining to classify news about supply and demand in the coffee market

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorLima Junior, Paulo Oliveira-
Autor(es): dc.creatorCastro Junior, Luiz Gonzaga de-
Autor(es): dc.creatorZambalde, Andre Luiz-
Data de aceite: dc.date.accessioned2026-02-09T12:49:12Z-
Data de disponibilização: dc.date.available2026-02-09T12:49:12Z-
Data de envio: dc.date.issued2018-07-25-
Data de envio: dc.date.issued2018-07-25-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/29744-
Fonte completa do material: dc.identifierhttps://ieeexplore.ieee.org/document/7817009/-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1168884-
Descrição: dc.descriptionThis work verifies the feasibility of text classification using supervised machine learning method to promote the web news monitoring on factors that impact supply and demand for the coffee market. To this end, a device was develop that enables the empirical evaluation of the Naive Bayes method to sort news collected from the web according to the categories: positive or negative to supply and to demand. The tests show the feasibility of Naive Bayes classifier to identify factors that affect supply and demand in coffee market.-
Idioma: dc.languageen-
Publicador: dc.publisherIEEE Xplore-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceIEEE Latin America Transactions-
Palavras-chave: dc.subjectCoffee market-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectTextmining-
Título: dc.titleApplying textmining to classify news about supply and demand in the coffee market-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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