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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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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

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Title: Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation
Author: Cagnina, Leticia Rosso, Paolo
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
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 ...[+]
Subjects: Cross-domain evaluation , Deception detection , Intra-domain evaluation , Low dimensionality representation , Opinion spam
Copyrigths: Reserva de todos los derechos
Source:
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. (issn: 0218-4885 )
DOI: 10.1142/S0218488517400165
Publisher:
World Scientific
Publisher version: https://doi.org/10.1142/S0218488517400165
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
Thanks:
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.
Type: Artículo

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