Semantic Genetic Programming for Sentiment Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Graff:2015:NEO,

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

}

Genetic Programming entries for Mario Graff Guerrero Eric Sadit Tellez Hugo Jair Escalante Sabino Miranda-Jimenez

Citations