Automatic Detection of Satire in Twitter: A psycholinguistic-based approach

Handle URI:
http://hdl.handle.net/10754/623288
Title:
Automatic Detection of Satire in Twitter: A psycholinguistic-based approach
Authors:
Salas-Zárate, María del Pilar; Paredes-Valverde, Mario Andrés; Rodriguez-Garcia, Miguel Angel ( 0000-0001-6244-6532 ) ; Valencia-García, Rafael; Alor-Hernández, Giner
Abstract:
In recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Salas-Zárate M del P, Paredes-Valverde MA, Rodriguez-García MÁ, Valencia-García R, Alor-Hernández G (2017) Automatic Detection of Satire in Twitter: A psycholinguistic-based approach. Knowledge-Based Systems. Available: http://dx.doi.org/10.1016/j.knosys.2017.04.009.
Publisher:
Elsevier BV
Journal:
Knowledge-Based Systems
Issue Date:
24-Apr-2017
DOI:
10.1016/j.knosys.2017.04.009
Type:
Article
ISSN:
0950-7051
Sponsors:
This work has been supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER / ERDF) through project KBS4FIA (TIN2016-76323-R).María del Pilar Salas-Zárate and Mario Andrés Paredes-Valverde are supported by the National Council of Science and Technology (CONACYT), the Secretariat of Public Education (SEP) and the Mexican government.This work was also supported by Tecnológico Nacional de Mexico (TecNM) and Secretariat of Public Education (SEP) through PRODEP (Programa para el Desarrollo Profesional Docente, in Spanish).
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0950705117301855
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorSalas-Zárate, María del Pilaren
dc.contributor.authorParedes-Valverde, Mario Andrésen
dc.contributor.authorRodriguez-Garcia, Miguel Angelen
dc.contributor.authorValencia-García, Rafaelen
dc.contributor.authorAlor-Hernández, Gineren
dc.date.accessioned2017-04-30T10:16:59Z-
dc.date.available2017-04-30T10:16:59Z-
dc.date.issued2017-04-24en
dc.identifier.citationSalas-Zárate M del P, Paredes-Valverde MA, Rodriguez-García MÁ, Valencia-García R, Alor-Hernández G (2017) Automatic Detection of Satire in Twitter: A psycholinguistic-based approach. Knowledge-Based Systems. Available: http://dx.doi.org/10.1016/j.knosys.2017.04.009.en
dc.identifier.issn0950-7051en
dc.identifier.doi10.1016/j.knosys.2017.04.009en
dc.identifier.urihttp://hdl.handle.net/10754/623288-
dc.description.abstractIn recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire.en
dc.description.sponsorshipThis work has been supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER / ERDF) through project KBS4FIA (TIN2016-76323-R).María del Pilar Salas-Zárate and Mario Andrés Paredes-Valverde are supported by the National Council of Science and Technology (CONACYT), the Secretariat of Public Education (SEP) and the Mexican government.This work was also supported by Tecnológico Nacional de Mexico (TecNM) and Secretariat of Public Education (SEP) through PRODEP (Programa para el Desarrollo Profesional Docente, in Spanish).en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0950705117301855en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems, [, , (2017-04-24)] DOI: 10.1016/j.knosys.2017.04.009 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectComputational psycholinguisticsen
dc.subjectLIWCen
dc.subjectMachine learningen
dc.subjectSatireen
dc.subjectTwitteren
dc.titleAutomatic Detection of Satire in Twitter: A psycholinguistic-based approachen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalKnowledge-Based Systemsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartamento de Informática y Sistemas, Universidad de Murcia. Campus de Espinardo 30100 Murcia, Spainen
dc.contributor.institutionDivision of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Av. Oriente 9, No. 852, Col. E. Zapata, CP 94320 Orizaba, Veracruz, Mexicoen
kaust.authorRodriguez-Garcia, Miguel Angelen
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