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Recent trends in deep learning based personality detection

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Abstract

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.

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Notes

  1. The Great Hack, a recent documentary about the Cambridge Analytica data scandal

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Acknowledgements

We would like to thank Prof. Bharat M Deshpande for his valuable guidance. A. Gelbukh recognizes the support of the Instituto Politecnico Nacional via the Secretaria de Investigacion y Posgrado projects SIP 20196437 and SIP 20196021.

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  1. Gatsby Computational Neuroscience Unit, University College London, London, UK

    Yash Mehta

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico

    Navonil Majumder & Alexander Gelbukh

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

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Mehta, Y., Majumder, N., Gelbukh, A. et al. Recent trends in deep learning based personality detection. Artif Intell Rev 53, 2313–2339 (2020). https://doi.org/10.1007/s10462-019-09770-z

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