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Personality classification from text using bidirectional long short-term memory model

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Abstract

A personality is a blend of an individual’s psychological characteristics and qualities, displaying human behaviour. Recently, the development of computational models for personality recognition has received research scientists’ attention. Prior studies on personality trait prediction have used machine and deep learning techniques, which perform feature extraction but do not retain long-term dependencies. In this study, we apply a deep learning model, namely BiLSTM, that can maintain long-term dependencies in both forward and backward directions for personality prediction on a benchmark essay dataset. The suggested model outperforms current strategies in classifying the user’s personality attributes. With this research’s findings, firms may make better judgments about hiring personnel. They may also use the research findings to choose, manage, and optimize their strategies, activities, and commodities.

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Data Availability

The supplementary data used to support the findings of this study are included with the manuscript and can also availed from the first author upon request.

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Acknowledgements

This Research work was supported by Zayed University Research Incentives Fund # R21095.

Funding

This Research work was supported by Zayed University Research Incentives Fund # R21095.

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Authors and Affiliations

  1. College of Technological Innovation, Zayed University, Dubai, United Arab Emirates

    Asad Khattak, Muhammad Zubair Asghar & Usama Asghar

  2. Institute of Computing and Information Technology, Gomal University, D.I. Khan, KP, Pakistan

    Nosheen Jellani, Muhammad Zubair Asghar & Usama Asghar

Authors
  1. Asad Khattak
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  2. Nosheen Jellani
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  3. Muhammad Zubair Asghar
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  4. Usama Asghar
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Corresponding author

Correspondence to Muhammad Zubair Asghar.

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Khattak, A., Jellani, N., Asghar, M.Z. et al. Personality classification from text using bidirectional long short-term memory model. Multimed Tools Appl 83, 28849–28873 (2024). https://doi.org/10.1007/s11042-023-16661-7

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  • DOI: https://doi.org/10.1007/s11042-023-16661-7

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