Semantic Genetic Programming for Sentiment Analysis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Graff:2015:NEO,
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author = "Mario Graff and Eric S. Tellez and
Hugo Jair Escalante and Sabino Miranda-Jimenez",
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title = "Semantic Genetic Programming for Sentiment Analysis",
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booktitle = "NEO 2015: Results of the Numerical and Evolutionary
Optimization Workshop NEO 2015 held at September 23-25
2015 in Tijuana, Mexico",
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year = "2015",
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editor = "Oliver Schuetze and Leonardo Trujillo and
Pierrick Legrand and Yazmin Maldonado",
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volume = "663",
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series = "Studies in Computational Intelligence",
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pages = "43--65",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, semantic
genetic programming, sentiment analysis",
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isbn13 = "978-3-319-44003-3",
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URL = "http://link.springer.com/chapter/10.1007/978-3-319-44003-3_2",
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DOI = "doi:10.1007/978-3-319-44003-3_2",
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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 = "Published 2017",
- }
Genetic Programming entries for
Mario Graff Guerrero
Eric Sadit Tellez
Hugo Jair Escalante
Sabino Miranda-Jimenez
Citations