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NEO 2015 pp 43-65 | Cite as

Semantic Genetic Programming for Sentiment Analysis

  • Mario GraffEmail author
  • Eric S. Tellez
  • Hugo Jair Escalante
  • Sabino Miranda-Jiménez
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 663)

Abstract

Sentiment analysis is one of the most important tasks in text mining. This field has a high impact for government and private companies to support major decision-making policies. Even though Genetic Programming (GP) has been widely used to solve real world problems, GP is seldom used to tackle this trendy problem. This contribution starts rectifying this research gap by proposing a novel GP system, namely, Root Genetic Programming, and extending our previous genetic operators based on projections on the phenotype space. The results show that these systems are able to tackle this problem being competitive with other state-of-the-art classifiers, and, also, give insight to approach large scale problems represented on high dimensional spaces.

Keywords

Semantic crossover Sentiment analysis Genetic programming Text mining 

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Mario Graff
    • 1
    Email author
  • Eric S. Tellez
    • 1
  • Hugo Jair Escalante
    • 2
  • Sabino Miranda-Jiménez
    • 1
  1. 1.CONACYT INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y ComunicaciónAguascalientesMexico
  2. 2.Computer Science DepartmentInstituto Nacional de Astrofísica, Óptica y ElectrónicaCholulaMexico

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