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Text-based automatic personality prediction: a bibliographic review

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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.

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Notes

  1. More information in [46].

  2. A number of calculated readability measures based on simple surface characteristics of the text. These measures are basically linear regressions based on the number of words, syllables, and sentences.

  3. https://sites.google.com/michalkosinski.com/mypersonality

  4. https://github.com/emorynlp/character-mining.

  5. https://github.com/emorynlp/personality-detection.

  6. Available on https://www.kaggle.com/datasnaek/mbti-type/.

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Acknowledgements

This project is supported by a research grant from the University of Tabriz (number S/806).

Funding

This project is supported by a research grant of the University of Tabriz (number S/806).

Author information

Authors and Affiliations

  1. Computerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran

    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 Jahanbakhsh-Naghadeh

  2. Department of Software Engineering, Ayvansaray University, Istanbul, Turkey

    Taymaz Akan

  3. Clinical Informatics, Louisiana State University Health Sciences Center Shreveport, Shreveport, USA

    Taymaz Akan

  4. Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

    Mehrdad Ranjbar-Khadivi

  5. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Elnaz Zafarni-Moattar

  6. Department of Computer Engineering, Naghadeh Branch, Islamic Azad University, Naghadeh, Iran

    Zoleikha Jahanbakhsh-Naghadeh

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A Appendix

A Appendix

See Table 14 and Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15.

Table 14 An overview of Cattell’s 16 [91]
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Fig. 4
figure 4

The architecture of proposed model for representation of texts called C2W2S4PT (Character to Word to Sentence for Personality Traits). Dotted boxes indicate concatenation [56, 57]

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Fig. 5
figure 5

The proposed architectures in [77]

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Fig. 6
figure 6

The structure of proposed algorithm called HIE and text representation model [79]

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Fig. 7
figure 7

The architecture of proposed method in [66] called 2CLSTM

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Fig. 8
figure 8

Pseudo-multi-view co-training (PMC)-based framework for personality prediction [58]

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Fig. 9
figure 9

Three-layer architecture overview of proposed personality GCN [62]

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Fig. 10
figure 10

Overall framework of Personality2Vec [60]

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Fig. 11
figure 11

The architecture of Bagged SVM over BERT word embedding technique [74]

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Fig. 12
figure 12

SEPRNN (semantic-enhanced personality recognition neural network) [20]

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Fig. 13
figure 13

The schema of proposed method named Transformer-MD [76]

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Fig. 14
figure 14

A knowledge graph-based APP methods schemed in [63]

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Fig. 15
figure 15

The general architecture of KGrAt-Net proposed by [64]. KGrAt-Net is a three-phase text classification approach which is basically founded on knowledge graph attention network

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Feizi-Derakhshi, AR., Feizi-Derakhshi, MR., Ramezani, M. et al. Text-based automatic personality prediction: a bibliographic review. J Comput Soc Sc 5, 1555–1593 (2022). https://doi.org/10.1007/s42001-022-00178-4

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