{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:36:16Z","timestamp":1777703776462,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,7,9]],"date-time":"2018-07-09T00:00:00Z","timestamp":1531094400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,7,27]]},"abstract":"<jats:p>Usually, most works use and combine different methods for generating features in order to improve deception detection; nevertheless, they do not take into account the fact that features may change depending on the nature of text. In this research, a study on the effect of the polarity over the set of features generated for deception detection task was carried out. We implemented a polarity classifier to generate subsets of positive and negative opinions. Next, a semantic and lexical method were used over the subsets to generate features and construct vectors. It was proven that adding polarity information did not positively impacted on deception detection. However, partitioning datasets improved classification results. To classify subsets, attribute selection was implemented and a Bayesian classifier was fed with the resulting vectors. Research findings show that cues to deception are affected by the opinion polarity. In addition, this approach registered up to 86% f-measure.<\/jats:p>","DOI":"10.3233\/jifs-169610","type":"journal-article","created":{"date-parts":[[2018,7,10]],"date-time":"2018-07-10T14:32:12Z","timestamp":1531233132000},"page":"549-558","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Impact of polarity in deception detection"],"prefix":"10.1177","volume":"35","author":[{"given":"\u00c1ngel","family":"Hern\u00e1ndez-Casta\u00f1eda","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n (CIC), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City, Mexico"},{"name":"Tec. de Estudios Superiores de Tianguistenco (TEST), Tec. Nacional de M\u00e9xico (TecNM), Edo. de M\u00e9x., Mexico"}]},{"given":"Hiram","family":"Calvo","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n (CIC), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City, Mexico"}]},{"given":"Omar Ju\u00e1rez","family":"Gambino","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n (CIC), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City, Mexico"},{"name":"Escuela Superior de C\u00f3mputo (ESCOM), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City, Mexico"}]}],"member":"179","published-online":{"date-parts":[[2018,7,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1037\/0033-2909.129.1.74"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1460-2466.2011.01619.x"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/jip.121"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/0146167203029005010"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/E14-1030"},{"key":"e_1_3_2_7_2","first-page":"285","article-title":"Detection of opinion spam with character n-grams","author":"Fusilier D.H.","year":"2015","unstructured":"FusilierD.H., Montes-y G\u00f3mezM., RossoP. and CabreraR.G., Detection of opinion spam with character n-grams, in International Conference on Intelligent Text Processing and Computational Linguistics, Springer, 2015, pp. 285\u2013294.","journal-title":"International Conference on Intelligent Text Processing and Computational Linguistics"},{"key":"e_1_3_2_8_2","first-page":"171","volume-title":"Association for Computational Linguistics","author":"Feng S.","year":"2012","unstructured":"FengS., BanerjeeR. and ChoiY., Syntactic stylometry for deception detection, in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, Association for Computational Linguistics2012, pp. 171\u2013175."},{"key":"e_1_3_2_9_2","first-page":"1341","article-title":"Using deep linguistic features for finding deceptive opinion spam","author":"Xu Q.","year":"2012","unstructured":"XuQ. and ZhaoH., Using deep linguistic features for finding deceptive opinion spam, in COLING (Posters)2012, pp. 1341\u20131350.","journal-title":"COLING (Posters)"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1147"},{"key":"e_1_3_2_11_2","first-page":"58","article-title":"Classification of deceptive opinions using a low dimensionality representation","author":"Cagnina L.C.","year":"2015","unstructured":"CagninaL.C. and RossoP., Classification of deceptive opinions using a low dimensionality representation, in WASSA@ EMNLP2015, pp. 58\u201366.","journal-title":"WASSA@ EMNLP"},{"key":"e_1_3_2_12_2","first-page":"497","article-title":"Negative deceptive opinion spam","author":"Ott M.","year":"2013","unstructured":"OttM., CardieC. and HancockJ.T., Negative deceptive opinion spam, in HLT-NAACL2013, pp. 497\u2013501.","journal-title":"HLT-NAACL"},{"key":"e_1_3_2_13_2","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei D.M.","year":"2003","unstructured":"BleiD.M., NgA.Y. and JordanM.I., Latent dirichlet allocation, Journal of Machine Learning Research3 (2003), 993\u20131022.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-170882"},{"key":"e_1_3_2_15_2","unstructured":"PennebakerJ.W. 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