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dc.contributor.authorPacella, Den
dc.contributor.authorDell’Aquila, Een
dc.contributor.authorMarocco, Den
dc.contributor.authorFurnell, Sen
dc.date.accessioned2018-01-10T02:13:21Z
dc.date.available2018-01-10T02:13:21Z
dc.date.issued2017-01-01en
dc.identifier.isbn9783319674001en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/10026.1/10530
dc.description.abstract

© Springer International Publishing AG 2017. We present a natural language processing model that allows automatic classification and prediction of the user’s negotiation style during the interaction with virtual humans in a 3D game. We collected the sentences used in the interactions of the users with virtual artificial agents and their associated negotiation style as measured by ROCI-II test. We analyzed the documents containing the sentences for each style applying text mining techniques and found statistical differences among the styles in agreement with their theoretical definitions. Finally, we trained two machine learning classifiers on the two datasets using pre-trained Word2Vec embeddings.

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dc.format.extent339 - 342en
dc.language.isoenen
dc.titleToward an automatic classification of negotiation styles using natural language processingen
dc.typeConference Contribution
plymouth.volume10498 LNAIen
plymouth.publication-statusPublisheden
plymouth.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.identifier.doi10.1007/978-3-319-67401-8_43en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Computing, Electronics and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
dc.identifier.eissn1611-3349en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1007/978-3-319-67401-8_43en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeConference Paper/Proceeding/Abstracten


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