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Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation

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Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation

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dc.contributor.author Cagnina, Leticia es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2020-10-17T03:32:22Z
dc.date.available 2020-10-17T03:32:22Z
dc.date.issued 2017-12 es_ES
dc.identifier.issn 0218-4885 es_ES
dc.identifier.uri http://hdl.handle.net/10251/152268
dc.description Electronic versíon of an article published as International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 25, 2, 2017, 151-174. DOI:10.1142/S0218488517400165 © copyright World Scientific Publishing Company. https://www.worldscientific.com/worldscinet/ijufks es_ES
dc.description.abstract [EN] Online opinions play an important role for customers and companies because of the increasing use they do to make purchase and business decisions. A consequence of that is the growing tendency to post fake reviews in order to change purchase decisions and opinions about products and services. Therefore, it is really important to filter out deceptive comments from the retrieved opinions. In this paper we propose the character n-grams in tokens, an efficient and effective variant of the traditional character n-grams model, which we use to obtain a low dimensionality representation of opinions. A Support Vector Machines classifier was used to evaluate our proposal on available corpora with reviews of hotels, doctors and restaurants. In order to study the performance of our model, we make experiments with intra and cross-domain cases. The aim of the latter experiment is to evaluate our approach in a realistic cross-domain scenario where deceptive opinions are available in a domain but not in another one. After comparing our method with state-of-the-art ones we may conclude that using character n-grams in tokens allows to obtain competitive results with a low dimensionality representation. es_ES
dc.description.sponsorship This publication was made possible by NPRP grant #9-175-1-033 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. es_ES
dc.language Inglés es_ES
dc.publisher World Scientific es_ES
dc.relation QNRF/NPRP 9-175-1-033 es_ES
dc.relation.ispartof International Journal of Uncertainty Fuzziness and Knowledge-Based Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Cross-domain evaluation es_ES
dc.subject Deception detection es_ES
dc.subject Intra-domain evaluation es_ES
dc.subject Low dimensionality representation es_ES
dc.subject Opinion spam es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1142/S0218488517400165 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Cagnina, L.; Rosso, P. (2017). Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 25(2):151-174. https://doi.org/10.1142/S0218488517400165 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1142/S0218488517400165 es_ES
dc.description.upvformatpinicio 151 es_ES
dc.description.upvformatpfin 174 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 25 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela 358203 es_ES
dc.contributor.funder Qatar National Research Fund es_ES


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