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dc.contributor.authorHarish, B S
dc.contributor.authorKumar, Keerthi
dc.date2019-12
dc.date.accessioned2022-03-17T09:20:48Z
dc.date.available2022-03-17T09:20:48Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12659
dc.description.abstractWith the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 5, nº 7
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2732es_ES
dc.rightsopenAccesses_ES
dc.subjectclassificationes_ES
dc.subjectclusteringes_ES
dc.subjectfeature selectiones_ES
dc.subjectensemble methodses_ES
dc.subjectsentiment analysises_ES
dc.subjectfeature fusiones_ES
dc.subjectironyes_ES
dc.subjectK-meanses_ES
dc.subjectIJIMAIes_ES
dc.titleAutomatic Irony Detection using Feature Fusion and Ensemble Classifieres_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttp://doi.org/10.9781/ijimai.2019.07.002


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