Predicting personality traits with semantic structures and LSTM-based neural networks

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buir.contributor.authorÜrgen, Burcu Ayşen
buir.contributor.orcidÜrgen, Burcu Ayşen|0000-0001-9664-0309
dc.citation.epage8025en_US
dc.citation.issueNumber10en_US
dc.citation.spage8007en_US
dc.citation.volumeNumber61en_US
dc.contributor.authorKosan, Muhammed Ali
dc.contributor.authorKaracan, Hacer
dc.contributor.authorÜrgen, Burcu Ayşen
dc.date.accessioned2023-02-27T16:31:26Z
dc.date.available2023-02-27T16:31:26Z
dc.date.issued2022-10
dc.departmentDepartment of Psychologyen_US
dc.description.abstractThere is a need to obtain more information about target audiences in many areas such as law enforcement agencies, institutions, human resources, and advertising agencies. In this context, in addition to the information provided by individuals, their personal characteristics are also important. In particular, the predictability of personality traits of individuals is seen as a major parameter in making decisions about individuals. Textual and media data in social media, where people produce the most data, can provide clues about people's personal lives, characteristics, and personalities. Each social media environment may contain different assets and structures. Therefore, it is important to make a structural analysis according to the social media platform. There is also a need for a labelled dataset to develop a model that can predict personality traits from social media data. In this study, first, a personality dataset was created which was retrieved from Twitter and labelled with IBM Personality Insight. Then the unstructured data were transformed into meaningful and processable data, LSTM-based prediction models were created with the structural analysis, and evaluations were made on both our dataset and PAN-2015-EN. © 2022 THE AUTHORSen_US
dc.description.provenanceSubmitted by Cem Çağatay Akgün (cem.akgun@bilkent.edu.tr) on 2023-02-27T16:31:26Z No. of bitstreams: 1 Predicting_personality_traits_with_semantic_structures_and_LSTM-based_neural_networks.pdf: 2912244 bytes, checksum: fb728e1c2bf56d2fc46cb1353d358b14 (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-27T16:31:26Z (GMT). No. of bitstreams: 1 Predicting_personality_traits_with_semantic_structures_and_LSTM-based_neural_networks.pdf: 2912244 bytes, checksum: fb728e1c2bf56d2fc46cb1353d358b14 (MD5) Previous issue date: 2022-10en
dc.identifier.doi10.1016/j.aej.2022.01.050en_US
dc.identifier.issn11100168
dc.identifier.urihttp://hdl.handle.net/11693/111844
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.aej.2022.01.050en_US
dc.source.titleAlexandria Engineering Journalen_US
dc.subjectFastTexten_US
dc.subjectLSTMen_US
dc.subjectPersonality dataseten_US
dc.subjectPersonality traitsen_US
dc.subjectPredictionen_US
dc.subjectPreprocessingen_US
dc.titlePredicting personality traits with semantic structures and LSTM-based neural networksen_US
dc.typeArticleen_US

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Department of Psychology
Aysel Sabuncu Brain Research Center (BAM)
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