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Detection of opinion spam with character n-grams

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Detection of opinion spam with character n-grams

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dc.contributor.author Hernández Fusilier, Donato es_ES
dc.contributor.author Montes Gomez, Manuel es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.contributor.author Guzmán Cabrera, Rafael es_ES
dc.date.accessioned 2016-05-19T08:04:51Z
dc.date.available 2016-05-19T08:04:51Z
dc.date.issued 2015
dc.identifier.isbn 978-3-319-18116-5
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10251/64360
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-18117-2_21 es_ES
dc.description.abstract In this paper we consider the detection of opinion spam as a stylistic classi cation task because, given a particular domain, the deceptive and truthful opinions are similar in content but di ffer in the way opinions are written (style). Particularly, we propose using character ngrams as features since they have shown to capture lexical content as well as stylistic information. We evaluated our approach on a standard corpus composed of 1600 hotel reviews, considering positive and negative reviews. We compared the results obtained with character n-grams against the ones with word n-grams. Moreover, we evaluated the e ffectiveness of character n-grams decreasing the training set size in order to simulate real training conditions. The results obtained show that character n-grams are good features for the detection of opinion spam; they seem to be able to capture better than word n-grams the content of deceptive opinions and the writing style of the deceiver. In particular, results show an improvement of 2:3% and 2:1% over the word-based representations in the detection of positive and negative deceptive opinions respectively. Furthermore, character n-grams allow to obtain a good performance also with a very small training corpus. Using only 25% of the training set, a Na ve Bayes classi er showed F1 values up to 0.80 for both opinion polarities. es_ES
dc.description.sponsorship This work is the result of the collaboration in the frame-work of the WIQEI IRSES project (Grant No. 269180) within the FP7 Marie Curie. The second author was partially supported by the LACCIR programme under project ID R1212LAC006. Accordingly, the work of the third author was in the framework the DIANA-APPLICATIONS-Finding Hidden Knowledge inTexts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. es_ES
dc.language Inglés es_ES
dc.publisher Springer International Publishing es_ES
dc.relation.ispartof Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;9042
dc.rights Reserva de todos los derechos es_ES
dc.subject Opinion spam es_ES
dc.subject Deceptive detection es_ES
dc.subject Character n-grams es_ES
dc.subject Word n-grams es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Detection of opinion spam with character n-grams es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-319-18117-2_21
dc.relation.projectID info:eu-repo/grantAgreement/LACCIR//R1212LAC006/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269180/EU/Web Information Quality Evaluation Initiative/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2012-38603-C02-01/ES/DIANA-APPLICATIONS: FINDING HIDDEN KNOWLEDGE IN TEXTS: APPLICATIONS/ 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 Hernández Fusilier, D.; Montes Gomez, M.; Rosso, P.; Guzmán Cabrera, R. (2015). Detection of opinion spam with character n-grams. En Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II. Springer International Publishing. 285-294. https://doi.org/10.1007/978-3-319-18117-2_21 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://link.springer.com/chapter/10.1007/978-3-319-18117-2_21 es_ES
dc.description.upvformatpinicio 285 es_ES
dc.description.upvformatpfin 294 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 306266 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Latin American and Caribbean Collaborative ICT Research Federation es_ES
dc.contributor.funder Universitat de València es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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