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Semantic Genetic Programming for Sentiment Analysis

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NEO 2015

Part of the book series: Studies in Computational Intelligence ((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.

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Notes

  1. 1.

    http://aci.info/2014/07/12/the-data-explosion-in-2014-minute-by-minute-infographic/.

  2. 2.

    The interested reader in how a document collection is processed to obtain a vector representation is referenced to the specialized literature [2, 28].

  3. 3.

    \(x_i\) could be a input-vector or a scalar.

  4. 4.

    The K-nearest neighbor classifier was tested with varying K from 10 to 100 and \(K=30\) gave the highest result.

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Author information

Authors and Affiliations

  1. CONACYT INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación, Aguascalientes, Mexico

    Mario Graff, Eric S. Tellez & Sabino Miranda-Jiménez

  2. Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Cholula, Mexico

    Hugo Jair Escalante

Authors
  1. Mario Graff
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  2. Eric S. Tellez
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  3. Hugo Jair Escalante
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  4. Sabino Miranda-Jiménez
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Corresponding author

Correspondence to Mario Graff .

Editor information

Editors and Affiliations

  1. Departamento de Comput Cinvestav-IPN, Mexico, Mexico

    Oliver Schütze

  2. Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  4. Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Mexico

    Yazmin Maldonado

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Graff, M., Tellez, E.S., Jair Escalante, H., Miranda-Jiménez, S. (2017). Semantic Genetic Programming for Sentiment Analysis. In: Schütze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds) NEO 2015. Studies in Computational Intelligence, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-319-44003-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-44003-3_2

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