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      • Department of Psychology
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      Predicting personality traits with semantic structures and LSTM-based neural networks

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      Author(s)
      Kosan, Muhammed Ali
      Karacan, Hacer
      Ürgen, Burcu Ayşen
      Date
      2022-10
      Source Title
      Alexandria Engineering Journal
      Print ISSN
      11100168
      Publisher
      Elsevier
      Volume
      61
      Issue
      10
      Pages
      8007 - 8025
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      There 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 AUTHORS
      Keywords
      FastText
      LSTM
      Personality dataset
      Personality traits
      Prediction
      Preprocessing
      Permalink
      http://hdl.handle.net/11693/111844
      Published Version (Please cite this version)
      https://dx.doi.org/10.1016/j.aej.2022.01.050
      Collections
      • Aysel Sabuncu Brain Research Center (BAM) 249
      • Department of Psychology 242
      • National Magnetic Resonance Research Center (UMRAM) 301
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