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dc.contributor.author
Cagnina, Leticia Cecilia
dc.contributor.author
Rosso, Paolo
dc.date.available
2019-10-03T20:11:47Z
dc.date.issued
2017-12
dc.identifier.citation
Cagnina, Leticia Cecilia; Rosso, Paolo; Detecting Deceptive Opinions: Intra and Cross-Domain Classification Using an Efficient Representation; World Scientific; International Journal Of Uncertainty, Fuzziness And Kb Systems; 25; 12-2017; 151-174
dc.identifier.issn
0218-4885
dc.identifier.uri
http://hdl.handle.net/11336/85167
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
World Scientific
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CROSS-DOMAIN EVALUATION
dc.subject
DECEPTION DETECTION
dc.subject
INTRA-DOMAIN EVALUATION
dc.subject
LOW DIMENSIONALITY REPRESENTATION
dc.subject
OPINION SPAM
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Detecting Deceptive Opinions: Intra and Cross-Domain Classification Using an Efficient Representation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-04-16T20:42:24Z
dc.journal.volume
25
dc.journal.pagination
151-174
dc.journal.pais
Singapur
dc.description.fil
Fil: Cagnina, Leticia Cecilia. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Rosso, Paolo. Universidad Politécnica de Valencia; España
dc.journal.title
International Journal Of Uncertainty, Fuzziness And Kb Systems
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1142/S0218488517400165
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.worldscientific.com/doi/abs/10.1142/S0218488517400165
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