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

Kosan M. A., Karacan H., Urgen B. A.

Alexandria Engineering Journal, cilt.61, sa.10, ss.8007-8025, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 61 Sayı: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.aej.2022.01.050
  • Dergi Adı: Alexandria Engineering Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.8007-8025
  • Anahtar Kelimeler: Personality traits, Prediction, LSTM, FastText, Preprocessing, Personality dataset
  • Gazi Üniversitesi Adresli: Evet

Özet

© 2022 THE AUTHORSThere 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.