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Text-based automatic personality prediction: a bibliographic review

Author

Listed:
  • Ali-Reza Feizi-Derakhshi

    (University of Tabriz)

  • Mohammad-Reza Feizi-Derakhshi

    (University of Tabriz)

  • Majid Ramezani

    (University of Tabriz)

  • Narjes Nikzad-Khasmakhi

    (University of Tabriz)

  • Meysam Asgari-Chenaghlu

    (University of Tabriz)

  • Taymaz Akan

    (University of Tabriz
    Ayvansaray University
    Louisiana State University Health Sciences Center Shreveport)

  • Mehrdad Ranjbar-Khadivi

    (University of Tabriz
    Islamic Azad University)

  • Elnaz Zafarni-Moattar

    (University of Tabriz
    Islamic Azad University)

  • Zoleikha Jahanbakhsh-Naghadeh

    (University of Tabriz
    Islamic Azad University)

Abstract

Personality detection is an old topic in psychology and automatic personality prediction (or perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, and video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three: pre-trained independent, pre-trained model based, and multimodal approaches. In addition, to achieve a comprehensive comparison, reported results are informed by datasets.

Suggested Citation

  • Ali-Reza Feizi-Derakhshi & Mohammad-Reza Feizi-Derakhshi & Majid Ramezani & Narjes Nikzad-Khasmakhi & Meysam Asgari-Chenaghlu & Taymaz Akan & Mehrdad Ranjbar-Khadivi & Elnaz Zafarni-Moattar & Zoleikha, 2022. "Text-based automatic personality prediction: a bibliographic review," Journal of Computational Social Science, Springer, vol. 5(2), pages 1555-1593, November.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:2:d:10.1007_s42001-022-00178-4
    DOI: 10.1007/s42001-022-00178-4
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