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Multidimensional and Multilingual Emotional Analysis

2024-02-15Documento de conferência Acesso aberto
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dc.contributor.authorAparício, Sofia
dc.contributor.authorAparício, João Tiago
dc.contributor.authorAparício, Manuela
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.coverage.spatialGewerbestrasse, Cham
dc.date.accessioned2024-02-20T23:55:14Z
dc.date.available2025-02-16T01:32:13Z
dc.date.embargoedUntil2025-02-15
dc.date.issued2024-02-15
dc.descriptionAparício, S., Aparício, J. T., & Aparício, M. (2024). Multidimensional and Multilingual Emotional Analysis. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & V. Colla (Eds.), Information Systems and Technologies: WorldCIST 2023, Volume 4 (Vol. 4, pp. 13-22). (Lecture Notes in Networks and Systems; Vol. 802). Springer. https://doi.org/10.1007/978-3-031-45651-0_2 --- We gratefully acknowledge financial support from FCT -Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant UIDB/04152/2020. This work is also supported by national funds through PhD grant (UI/BD/153587/2022) supported by FCT.
dc.description.abstractIn order to monitor informal political online discussions and to lead a better understanding of hate speech on social media, we found that it was necessary to use sentiment quantification for languages with few training datasets. Previous studies mainly rely on languages with enough data to train a model. Several statistical and machine learning models were produced and compared in three languages (English, Portuguese and Polish). This work shows promising results when inferring sentimental dimensions, even in languages other than English.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent10
dc.format.extent735439
dc.identifier.doi10.1007/978-3-031-45651-0_2
dc.identifier.isbn978-3-031-45650-3
dc.identifier.isbn978-3-031-45651-0
dc.identifier.issn2367-3370
dc.identifier.otherPURE: 83793207
dc.identifier.otherPURE UUID: 922ef2d4-084d-41af-8301-a5886a65b904
dc.identifier.otherScopus: 85187665525
dc.identifier.otherWOS: 001259466400002
dc.identifier.otherORCID: /0000-0003-4261-0344/work/153662148
dc.identifier.urihttp://hdl.handle.net/10362/163852
dc.identifier.urlhttps://github.com/keras-team/keras
dc.identifier.urlhttps://www.scopus.com/pages/publications/85187665525
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001259466400002
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectEmotional ratings of text
dc.subjectAffective norms
dc.subjectLong Short-Term Memory
dc.subjectRecurrent Neural Networks
dc.subjectMachine learning
dc.subjectControl and Systems Engineering
dc.subjectSignal Processing
dc.subjectComputer Networks and Communications
dc.subjectSDG 8 - Decent Work and Economic Growth
dc.subjectSDG 9 - Industry, Innovation, and Infrastructure
dc.titleMultidimensional and Multilingual Emotional Analysisen
dc.typeconference object
degois.publication.firstPage13
degois.publication.lastPage22
degois.publication.titleInformation Systems and Technologies
degois.publication.title11th World Conference on Information Systems and Technologies 2023
degois.publication.volume4
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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