Sentiment analysis with genetically evolved Gaussian kernels
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Roman:2019:GECCO,
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author = "Ibai Roman and Alexander Mendiburu and
Roberto Santana and Jose A. Lozano",
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title = "Sentiment analysis with genetically evolved {Gaussian}
kernels",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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isbn13 = "978-1-4503-6111-8",
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pages = "1328--1337",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3321707.3321779",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, NLP, Natural
language processing, Gaussian processes",
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size = "10 pages",
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abstract = "Sentiment analysis consists of evaluating opinions or
statements based on text analysis. Among the methods
used to estimate the degree to which a text expresses a
certain sentiment are those based on Gaussian
Processes. However, traditional Gaussian Processes
methods use a predefined kernels with hyper-parameters
that can be tuned but whose structure can not be
adapted. In this paper, we propose the application of
Genetic Programming for the evolution of Gaussian
Process kernels that are more precise for sentiment
analysis. We use use a very flexible representation of
kernels combined with a multi-objective approach that
considers simultaneously two quality metrics and the
computational time required to evaluate those kernels.
Our results show that the algorithm can outperform
Gaussian Processes with traditional kernels for some of
the sentiment analysis tasks considered.",
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notes = "Also known as \cite{3321779} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Ibai Roman
Alexander Mendiburu
Roberto Santana
Jose A Lozano
Citations