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Erschienen in:
An Introduction to Geometric Semantic Genetic Programming Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Semantic Genetic Programming for Sentiment Analysis

verfasst von : Mario Graff, Eric S. Tellez, Hugo Jair Escalante, Sabino Miranda-Jiménez

Erschienen in: NEO 2015

Verlag: Springer International Publishing

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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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Vorheriges Kapitel An Introduction to Geometric Semantic Genetic Programming
Nächstes Kapitel Local Search Approach to Genetic Programming for RF-PAs Modeling Implemented in FPGA
Fußnoten
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
\(x_i\) could be a input-vector or a scalar.
 
4
The K-nearest neighbor classifier was tested with varying K from 10 to 100 and \(K=30\) gave the highest result.
 
Literatur
1.
Arora, S., Mayfield, E., Penstein-Ros, C., Nyberg, E.: Sentiment classification using automatically extracted subgraph features. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, CAAGET ’10, pp. 131–139, Stroudsburg, PA, USA (2010). Association for Computational Linguistics. 00030
2.
Baeza-Yates, P.A., Ribeiro-Neto, B.A.: Modern Information Retrieval, 2 edn. Addison-Wesley (2011)
3.
Castelli, M., Silva, S., Vanneschi, L.: A C++ framework for geometric semantic genetic programming. Genet. Program. Evol. Mach. 16(1), 73–81 (2014). 00004CrossRef
4.
Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., Z-Flores, E., Legrand, P.: Geometric semantic genetic programming with local search. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, GECCO ’15, pp. 999–1006. ACM, New York, NY, USA (2015). 00000
5.
Doucette, J., Lichodzijewski, P., Heywood, M.: Evolving coevolutionary classifiers under large attribute spaces. In: Riolo, R., O’Reilly, U.-M., McConaghy, T. (eds.) Genetic Programming Theory and Practice VII, Genetic and Evolutionary Computation, pp. 37–54. Springer, US (2010). 00008. doi:10.​1007/​978-1-4419-1626-6_​3
6.
Escalante, H.J., Garcia-Limon, M.A., Morales-Reyes, A., Graff, M., Montes-y Gomez, M., Morales, E.F., Martinez-Carranza, J.: Term-weighting learning via genetic programming for text classification. Knowl.-Based Syst. (2015). 00000
7.
Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40(2):121–144 (2010)
8.
Giannakopoulos, G., Mavridi, P., Paliouras, G., Papadakis, G., Tserpes, K.: Representation models for text classification: a comparative analysis over three web document types. In: Proceedings of the 2Nd International Conference on Web Intelligence, Mining and Semantics, WIMS ’12, pp. 13:1–13:12. ACM, New York, NY, USA (2012)
9.
Graff, Mario, Tellez, E.S., Villasenor, E., Miranda-Jiménez, S.: Semantic genetic programming operators based on projections in the phenotype space. Res. Comput. Sci. 94, 73–85 (2015)
10.
Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2):65–70 (1979). 10011
11.
Iqbal, M., Browne, W.N., Zhang, M.: Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems. IEEE Trans. Evol. Comput. 18(4):465–480 (2014). 00019
12.
Korns, M.F.: Large-scale, time-constrained symbolic regression. In: Riolo, R., Soule, T., Worzel, B. (eds.) Genetic Programming Theory and Practice IV, Genetic and Evolutionary Computation, pp. 299–314. Springer, US (2007). 00019 doi:10.​1007/​978-0-387-49650-4_​18
13.
Korns, M.F.: Large-scale, time-constrained symbolic regression-classification. In: Riolo, R., Soule, T., Worzel, B. (eds.) Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation Series, pp. 53–68. Springer, US, (2008). 00020 doi:10.​1007/​978-0-387-76308-8_​4
14.
Korns, M.F., Nunez, L.: Profiling symbolic regression-classification. In: Genetic Programming Theory and Practice VI, Genetic and Evolutionary Computation, pp. 1–14. Springer, US (2009). 00011 doi:10.​1007/​978-0-387-87623-8_​14
15.
Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, 381 p. Cambridge University Press (2015). ISBN: 1-107-01789-0
16.
Mayfield, E., Penstein-Rosé, C.: Using feature construction to avoid large feature spaces in text classification. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO ’10, pp. 1299–1306. ACM, New York, NY, USA (2010). 00013
17.
McConaghy, T.: Latent variable symbolic regression for high-dimensional inputs. In: Riolo, R., O’Reilly, U.-M., McConaghy, T. (eds.) Genetic Programming Theory and Practice VII, Genetic and Evolutionary Computation, pp. 103–118. Springer, US (2010). 00007. doi:10.​1007/​978-1-4419-1626-6_​7
18.
Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) Parallel Problem Solving from Nature - PPSN XII, number 7491 in Lecture Notes in Computer Science, pp. 21–31. Springer, Berlin, Heidelberg (2012)
19.
Navarro, G., Raffinot, M.: Flexible Pattern Matching in Strings – Practical on-line search algorithms for texts and biological sequences, 280 p. Cambridge University Press (2002). ISBN 0-521-81307-7
20.
Padr, L., Stanilovsky, E.: Freeling 3.0: Towards wider multilinguality. In: Proceedings of the Language Resources and Evaluation Conference (LREC 2012). ELRA, Istanbul, Turkey (2012)
21.
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRef
22.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
23.
Peng, T., Zuo, W., He, F.: Svm based adaptive learning method for text classification from positive and unlabeled documents. Knowl. Inf. Syst. 16(3), 281–301 (2008)CrossRef
24.
25.
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises UK Ltd (2008)
26.
Romn, J.V., Morera, J.G., Garca Cumbreras, M.A., Martnez Cmara, E., Teresa Martn Valdivia, M., Alfonso Urea Lpez, L.: Overview of tass 2015. CEUR Workshop Proc. 1397:13–21 (2015)
27.
Sammut, C., Webb, G.I. (eds.): Statistical natural language processing. Encyclopedia of Machine Learning, pp. 916–916. Springer, US (2010)
28.
29.
Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)MathSciNetCrossRef
30.
Sidorov, G., Miranda-Jiménez, S., Viveros-Jiménez, F., Gelbukh, A., Castro-Sánchez, N., Velásquez, F., Díaz-Rangel, I., Suárez-Guerra, S., Treviño, A., Gordon, J.: Empirical study of machine learning based approach for opinion mining in tweets. In: Proceedings of the 11th Mexican International Conference on Advances in Artificial Intelligence - Volume Part I, MICAI’12, pp. 1–14. Springer, Berlin, Heidelberg (2013)
31.
Silla, C.N. Jr., Pappa, G.L., Freitas, A.A., Kaestner, A.A.: Automatic text summarization with genetic algorithm-based attribute selection. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) Proceedings 9th Ibero-American Conference on AI Advances in Artificial Intelligence - IBERAMIA 2004. Lecture Notes in Computer Science, vol. 3315, pp. 305–314. Springer, Puebla, Mexico, 22–26 November 2004
32.
33.
Uy, N.Q., Anh, P.T., Doan, T.C., Hoai, N.X.: A study on the use of genetic programming for automatic text summarization. In: Dang-Van, H., Sanders, J. (eds.) The Fourth International Conference on Knowledge and Systems Engineering, KSE 2012, pp. 93–98, Danang, Vietnam, 17–19 August 2012
34.
Vanneschi, L., Castelli, M., Manzoni, L., Silva, S.: A new implementation of geometric semantic GP and its application to problems in pharmacokinetics. In: Krawiec, K., Moraglio, A., Hu, T., Ima Etaner-Uyar, A., Hu, B. (eds.) Genetic Programming, number 7831 in Lecture Notes in Computer Science, pp. 205–216. Springer, Berlin, Heidelberg (2013)
35.
Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program. Evol. Mach. 15(2), 195–214 (2014). JuneCrossRef
36.
White, D.R.: Software review: the ecj toolkit. Genet. Program. Evol. Mach. 13(1):65–67 (2012)
37.
Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80 (1945)CrossRef
38.
Zhang, Y., Bhattacharyya, S.: Genetic programming in classifying large-scale data: an ensemble method. Inf. Sci. 163(1–3):85–101 (2004). 00061
39.
Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms. CRC Press (2012)
Metadaten
Titel
Semantic Genetic Programming for Sentiment Analysis
verfasst von
Mario Graff
Eric S. Tellez
Hugo Jair Escalante
Sabino Miranda-Jiménez
Copyright-Jahr
2017
Buch
NEO 2015

Print ISBN: 978-3-319-44002-6

Electronic ISBN: 978-3-319-44003-3

Copyright-Jahr: 2017

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

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