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Extended Association Rules in Semantic Vector Spaces for Sentiment Classification

  • Brian Keith Norambuena
  • Claudio Meneses Villegas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

Sentiment analysis is a field that has experienced considerable growth over the last decade. This area of research attempts to determine the opinions of people on something or someone. This article introduces a novel technique for association rule extraction in text called Extended Association Rules in Semantic Vector Spaces (AR-SVS). This new method is based on the construction of association rules, which are extended through a similarity criteria for terms represented in a semantic vector space. The method was evaluated on a sentiment analysis data set consisting of scientific paper reviews. A quantitative and qualitative analysis is done with respect to the classification performance and the generated rules. The results show that the method is competitive with respect to the baseline provided by NB and SVM.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Brian Keith Norambuena
    • 1
  • Claudio Meneses Villegas
    • 1
  1. 1.Departamento de Ingeniería de Sistemas y ComputaciónUniversidad Católica del NorteAntofagastaChile

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