Aplicando Text Mining para Classificar Notícias sobre Oferta e Demanda no Mercado de Café (Applying Textmining to Classify News About Supply and Demand in the Coffee Market)

Paulo Oliveira Lima Júnior (plima@nepomuceno.cefetmg.br)1, Luiz Gonzaga de Castro Júnior (gonzaga.ufla@gmail.com)2, André Luiz Zambalde (zamba@dcc.ufla.br)2

1Centro Federal de Educação Tecnológica de Minas Gerais
2Universidade Federal de Lavras

This paper appears in: Revista IEEE América Latina

Publication Date: Dec. 2016
Volume: 14,   Issue: 12 
ISSN: 1548-0992

This 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

Index Terms:
Textmining, Machine Learning, Coffee Market   

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