Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155153
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dc.contributor.authorMehta, Yashen_US
dc.contributor.authorMajumder, Navonilen_US
dc.contributor.authorGelbukh, Alexanderen_US
dc.contributor.authorCambria, Eriken_US
dc.date.accessioned2022-02-14T08:01:45Z-
dc.date.available2022-02-14T08:01:45Z-
dc.date.issued2020-
dc.identifier.citationMehta, Y., Majumder, N., Gelbukh, A. & Cambria, E. (2020). Recent trends in deep learning based personality detection. Artificial Intelligence Review, 53(4), 2313-2339. https://dx.doi.org/10.1007/s10462-019-09770-zen_US
dc.identifier.issn0269-2821en_US
dc.identifier.urihttps://hdl.handle.net/10356/155153-
dc.description.abstractRecently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.en_US
dc.language.isoenen_US
dc.relation.ispartofArtificial Intelligence Reviewen_US
dc.rights© 2019 Springer Nature B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleRecent trends in deep learning based personality detectionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1007/s10462-019-09770-z-
dc.identifier.scopus2-s2.0-85074520909-
dc.identifier.issue4en_US
dc.identifier.volume53en_US
dc.identifier.spage2313en_US
dc.identifier.epage2339en_US
dc.subject.keywordsPersonality Detectionen_US
dc.subject.keywordsMultimodal Interactionen_US
dc.description.acknowledgementA. Gelbukh recognizes the support of the Instituto Politecnico Nacional via the Secretaria de Investigacion y Posgrado projects SIP 20196437 and SIP 20196021.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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