A method for detecting the profile of an author

This paper presents a method for detecting an author’s profile using the following two elements: gender and age. This is based on a set of dialogues, written in two languages: English and Spanish, provided for Author Profiling competence within the evaluation forum "Uncovering Plagiarism, Autho...

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Autores:
Silva, Jesus
García, Silvia
Binda, María Alejandra
Marin Gonzalez, Fredy
Barrios, Rosio
Leon Castro, Bellanit
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7788
Acceso en línea:
https://hdl.handle.net/11323/7788
https://doi.org/10.1016/j.procs.2020.03.101
https://repositorio.cuc.edu.co/
Palabra clave:
Supervised Classification
PAN 2018
Gender
Age
Random forest
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv A method for detecting the profile of an author
title A method for detecting the profile of an author
spellingShingle A method for detecting the profile of an author
Supervised Classification
PAN 2018
Gender
Age
Random forest
title_short A method for detecting the profile of an author
title_full A method for detecting the profile of an author
title_fullStr A method for detecting the profile of an author
title_full_unstemmed A method for detecting the profile of an author
title_sort A method for detecting the profile of an author
dc.creator.fl_str_mv Silva, Jesus
García, Silvia
Binda, María Alejandra
Marin Gonzalez, Fredy
Barrios, Rosio
Leon Castro, Bellanit
dc.contributor.author.spa.fl_str_mv Silva, Jesus
García, Silvia
Binda, María Alejandra
Marin Gonzalez, Fredy
Barrios, Rosio
Leon Castro, Bellanit
dc.subject.spa.fl_str_mv Supervised Classification
PAN 2018
Gender
Age
Random forest
topic Supervised Classification
PAN 2018
Gender
Age
Random forest
description This paper presents a method for detecting an author’s profile using the following two elements: gender and age. This is based on a set of dialogues, written in two languages: English and Spanish, provided for Author Profiling competence within the evaluation forum "Uncovering Plagiarism, Authorship, and Social Software Misuse" (PAN2018). Counts of lexical, semantic, and syntactic characteristics are used to generate a two-phase classification system, which first classifies gender and then age. The results obtained show that, with the amount of data available, it is possible to characterize both the age and gender of an author with an accuracy greater than 50%. However, these values could be improved by having more evidence of information in the training data.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-28T13:02:18Z
dc.date.available.none.fl_str_mv 2021-01-28T13:02:18Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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url https://hdl.handle.net/11323/7788
https://doi.org/10.1016/j.procs.2020.03.101
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
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language eng
dc.relation.references.spa.fl_str_mv 1 Schler, J., Koppel, M., Argamon, S., Pennebaker, J.W.: Effects of age and gender on blogging. In: Computational Approaches to Analyzing Weblogs, Papers from the 2006 AAAI Spring Symposium, Technical Report SS-06-03, Stanford, California, USA, March 27-29, 2006. (2006) 199–205
2 Argamon S., Koppel M., Pennebaker J.W., Schler J. Automatically profiling the author of an anonymous text Commun. ACM, 52 (2) (2009), pp. 119-123 (February)
3 Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents. SMUC ‘11, New York, NY, USA, ACM (2011) 37–44
4 Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “how old do you think i am?”: A study of language and age in twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. ICWSM 2013 (2013)
5 Rangel, F., Rosso, P.: Use of language and author profiling: Identification of gender and age. In: Proceedings of the 10th Workshop on Natural Language Processing and Cognitive Science (NLPCS-2013). (2013)
6 Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Procee- dings of the International Conference on New Methods in Language Processing, Manchester, UK (1994)
7 Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Human Language Technology Conference (HLT-NAACL 2003). (2003)
8 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)
9 De Werra D. An introduction to timetabling European Journal of Operational Research, 19 (2) (1985), pp. 151-162
10 Obit, J.H., Ouelhadj, D., Landa-Silva, D., Vun, T.K., & Alfred, R.: Designing a multi- agent approach system for distributed course timetabling, pp. 103–108, doi:10.1109/HIS.2011.6122088 (2011)
11 Lewis, M.R.R.: Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University (2006)
12 Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., & Deng, Y.: An application of genetic al- gorithm for university course timetabling problem, pp. 2119-2122, doi:10.1109/CCDC.2011.5968555 (2011)
13 Mahiba A.A., Durai C.A.D. Genetic algorithm with search bank strategies for universi- ty course timetabling problem Procedia Engineering, 38 (2012), pp. 253-263
14 Soria-Alcaraz, J.A.; Carpio, J.M.; Puga, Hé.; Melin, P.; Terashima-Marn, H.; Reyes, L.
15 C. Sotelo-Figueroa, M.A. Castillo, O.; Melin P., Pedrycz W., Kacprzyk J. Generic Memetic Algorithm for Course Timetabling ITC2007 Recent Advances on Hybrid Approaches for Designing Intelligent Systems, Springer (2014), pp. 481-492 vol. 547
16 Nguyen, K., Lu, T., Le, T., & Tran, N.: Memetic algorithm for a university course timeta- bling problem. pp. 67-71, doi:10.1007/978-3-642-25899-2_10 (2011)
Aladag, C., & Hocaoglu, G.: A tabu search algorithm to solve a course timetabling prob- lem. Hacettepe journal of mathematics and statistics, pp. 53–64 (2007)
18 Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826) (1989).
19 Viloria Amelec, Lezama Omar Bonerge Pineda Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-1206
20 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernández, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.
21 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580
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spelling Silva, JesusGarcía, SilviaBinda, María AlejandraMarin Gonzalez, FredyBarrios, RosioLeon Castro, Bellanit2021-01-28T13:02:18Z2021-01-28T13:02:18Z2020https://hdl.handle.net/11323/7788https://doi.org/10.1016/j.procs.2020.03.101Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This paper presents a method for detecting an author’s profile using the following two elements: gender and age. This is based on a set of dialogues, written in two languages: English and Spanish, provided for Author Profiling competence within the evaluation forum "Uncovering Plagiarism, Authorship, and Social Software Misuse" (PAN2018). Counts of lexical, semantic, and syntactic characteristics are used to generate a two-phase classification system, which first classifies gender and then age. The results obtained show that, with the amount of data available, it is possible to characterize both the age and gender of an author with an accuracy greater than 50%. However, these values could be improved by having more evidence of information in the training data.Silva, JesusGarcía, SilviaBinda, María AlejandraMarin Gonzalez, FredyBarrios, RosioLeon Castro, Bellanitapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305391#!Supervised ClassificationPAN 2018GenderAgeRandom forestA method for detecting the profile of an authorArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 Schler, J., Koppel, M., Argamon, S., Pennebaker, J.W.: Effects of age and gender on blogging. In: Computational Approaches to Analyzing Weblogs, Papers from the 2006 AAAI Spring Symposium, Technical Report SS-06-03, Stanford, California, USA, March 27-29, 2006. (2006) 199–2052 Argamon S., Koppel M., Pennebaker J.W., Schler J. Automatically profiling the author of an anonymous text Commun. ACM, 52 (2) (2009), pp. 119-123 (February)3 Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents. SMUC ‘11, New York, NY, USA, ACM (2011) 37–444 Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “how old do you think i am?”: A study of language and age in twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. ICWSM 2013 (2013)5 Rangel, F., Rosso, P.: Use of language and author profiling: Identification of gender and age. In: Proceedings of the 10th Workshop on Natural Language Processing and Cognitive Science (NLPCS-2013). (2013)6 Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Procee- dings of the International Conference on New Methods in Language Processing, Manchester, UK (1994)7 Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Human Language Technology Conference (HLT-NAACL 2003). (2003)8 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)9 De Werra D. An introduction to timetabling European Journal of Operational Research, 19 (2) (1985), pp. 151-16210 Obit, J.H., Ouelhadj, D., Landa-Silva, D., Vun, T.K., & Alfred, R.: Designing a multi- agent approach system for distributed course timetabling, pp. 103–108, doi:10.1109/HIS.2011.6122088 (2011)11 Lewis, M.R.R.: Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University (2006)12 Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., & Deng, Y.: An application of genetic al- gorithm for university course timetabling problem, pp. 2119-2122, doi:10.1109/CCDC.2011.5968555 (2011)13 Mahiba A.A., Durai C.A.D. Genetic algorithm with search bank strategies for universi- ty course timetabling problem Procedia Engineering, 38 (2012), pp. 253-26314 Soria-Alcaraz, J.A.; Carpio, J.M.; Puga, Hé.; Melin, P.; Terashima-Marn, H.; Reyes, L.15 C. Sotelo-Figueroa, M.A. Castillo, O.; Melin P., Pedrycz W., Kacprzyk J. Generic Memetic Algorithm for Course Timetabling ITC2007 Recent Advances on Hybrid Approaches for Designing Intelligent Systems, Springer (2014), pp. 481-492 vol. 54716 Nguyen, K., Lu, T., Le, T., & Tran, N.: Memetic algorithm for a university course timeta- bling problem. pp. 67-71, doi:10.1007/978-3-642-25899-2_10 (2011)Aladag, C., & Hocaoglu, G.: A tabu search algorithm to solve a course timetabling prob- lem. Hacettepe journal of mathematics and statistics, pp. 53–64 (2007)18 Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826) (1989).19 Viloria Amelec, Lezama Omar Bonerge Pineda Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-120620 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernández, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.21 Viloria Amelec, et al. 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