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dc.contributor.authorOrasan, Constantin
dc.date.accessioned2018-10-03T14:48:35Z
dc.date.available2018-10-03T14:48:35Z
dc.date.issued2018-06-25
dc.identifier.urihttp://hdl.handle.net/2436/621749
dc.description.abstractThis paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods.
dc.formatapplication/PDF
dc.language.isoen
dc.publisherAssociation for Computational Linguistics
dc.relation.urlhttps://aclanthology.info/volumes/proceedings-of-the-first-workshop-on-trolling-aggression-and-cyberbullying-trac-2018
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subjectaggression identification
dc.subjectword embeddings
dc.subjectsentiment analysis
dc.titleAggressive language identification using word embeddings and sentiment features
dc.typeConference contribution
dc.identifier.journalProceedings of the First Workshop on Trolling, Aggression and Cyberbullying
dc.conference.locationSanta Fe, New Mexico, USA
pubs.finish-date2018-08-25
pubs.place-of-publicationMassachusetts Institute of Technology (MIT), Cambridge, Massachusetts
pubs.start-date2018-08-25
dc.date.accepted2018-06-25
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.projectUOW11092018CO
rioxxterms.versionAM
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2018-06-25
refterms.dateFCD2018-09-11T14:42:42Z
refterms.versionFCDAM
refterms.dateFOA2018-10-25T10:02:28Z


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