Análise de Técnicas de Aprendizado de Máquina para Classificar Notícias para Gerencimento de Informação no Mercado de Café
(Analysis of Machine Learning Techniques to Classify News for Information Management in Coffee Market)
Paulo Oliveira Lima Júnior (email@example.com)1, Luiz Gonzada de Castro Júnior (firstname.lastname@example.org)2, André Luiz Zambalde (email@example.com)2
1Centro Federal de Educação Tecnológica de Minas Gerais2Universidade Federal de Lavras
This paper appears in: Revista IEEE América Latina
Publication Date: July 2015
Volume: 13, Issue: 7
This paper presents an empirical study of machine learn techniques to text categorization. Specifically aim to classify news about coffee market according with categories from coffee supply chain. The objective is to measure the performance of three types of algorithms: Naïve Bayes based, Tree bases and Support Vector Machine (SVM). A database with news collected from web and labeled by human expert analysts is used in a learning phase. Then automatic classify news extracted from web following the same steps and terms as human according to their relevance for each learned category. The test in a real database shows a better performance by Naïve Bayes based Algorithms for this specific case.
Information Management, Text Categorization, Machine Learning
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