Applying Textmining to Classify News About Supply and Demand in the Coffee Market

Applying Textmining to Classify News About Supply and Demand in the Coffee Market

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 s...

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Bibliographic Details
Published in:Revista IEEE América Latina Vol. 14; no. 12; pp. 4768 - 4774
Main Authors: Oliveira Lima Junior, Paulo, Gonzaga de Castro Junior, Luiz, Luiz Zambalde, Andre
Format: Journal Article
Language:English
Published: Los Alamitos IEEE 01-12-2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Bayes methods
Coffee
Coffee industry
Coffee Market
Coffeehouses
Data mining
Feasibility
IEEE transactions
Laser radar
Machine Learning
Manuals
Monitoring
News
Supply & demand
Supply and demand
Textmining
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Summary: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
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2016.7817009