Skip to main content
SpringerLink
Log in

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

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Algorithm 1
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

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.

References

  1. Majumder N, Poria S, Gelbukh A, Cambria E (2017) Deep Learning-Based Document Modeling for Personality Detection from Text. IEEE Intell Syst 32(2):74–79. https://doi.org/10.1109/MIS.2017.23

    Article  Google Scholar 

  2. Sharma S, Kumar P, Kumar K (2017) LEXER: LEXicon based emotion AnalyzeR. In: Lecture notes in computer science. Springer International Publishing, pp 373–379

  3. Ahmad H, Asghar MU, Asghar MZ, Khan A, Mosavi AH (2021) A Hybrid Deep Learning Technique for Personality Trait Classification From Text. IEEE Access 9:146214–146232. https://doi.org/10.1109/ACCESS.2021.3121791

    Article  Google Scholar 

  4. Golbeck, J, Robles, C, Edmondson, M, Turner, K (2011) Predicting personality from twitter. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing. IEEE (pp. 149-156). https://doi.org/10.1109/PASSAT/SocialCom.2011.33

  5. Gjurković, M, Šnajder, J (2018) Reddit: A gold mine for personality prediction. In Proceedings of the second workshop on computational modeling of people’s opinions, personality, and emotions in social media. New Orleans, Louisiana, USA. Association for Computational Linguistics (pp. 87-97). https://doi.org/10.18653/v1/W18-1112

  6. Choong EJ, Varathan KD (2021) Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum. PeerJ 9(e11382):e11382. https://doi.org/10.7717/peerj.11382

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kumar K (2023) R3 N2: a novel approach for review based custom star rating using recurrent neural network. J Ambient Intell Humaniz Comput 14(7):9089–9097

    Article  Google Scholar 

  8. Kumar A, Purohit K, Kumar K (2021) Stock price prediction using recurrent neural network and long short-term memory. In: Conference Proceedings of ICDLAIR2019. Springer International Publishing, pp 153–160

  9. Kumar S, Kumar K (2018) IRSC: integrated automated review mining system using virtual machines in cloud environment. 2018 Conference on Information and Communication

  10. Ansari H, Vijayvergia A, Kumar K (2018) DCR-HMM: depression detection based on content rating using hidden markov model. 2018 Conference on Information and Communication Technology (CICT)

  11. Khan AS, Ahmad H, Zubair M, Khan F, Arif A, Ali H (2020) Personality classification from online text using machine learning approach. Int J Adv Comput Sci Appl 11(3). https://doi.org/10.14569/ijacsa.2020.0110358

  12. Sharma S, Kumar K, Singh N (2022) Deep eigen space based ASL recognition system. IETE J Res 68(5):3798–3808

    Article  Google Scholar 

  13. Bharadwaj, S, Sridhar, S, Choudhary, R, Srinath, R (2018) Persona traits identification based on Myers-Briggs Type Indicator (MBTI)-a text classification approach. In 2018 international conference on advances in computing, communications and informatics (ICACCI) (pp. 1076-1082). IEEE. https://doi.org/10.1109/ICACCI.2018.8554828

  14. Kumar K, Kurhekar M (2017) Sentimentalizer: docker container utility over cloud. 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)

  15. Dhiman A, Kumar D (2018) Sentiment analysis approach based N-gram and KNN classifier. Int J Comput Appl 182(4):29–32. https://doi.org/10.5120/ijca2018917513

    Article  Google Scholar 

  16. Chaudhary S, Sing R, Hasan ST, Kaur I (2018) A comparative Study of Different Classifiers for Myers-Brigg Personality Prediction Model. IRJET 05:1410–1413

    Google Scholar 

  17. Ilmini WMKS, Fernando TGI (2023) Explaining the Outputs of Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) based Apparent Personality Detection Models using the Class Activation Maps. Int J Adv Comput Sci Appl 14(2):25–36. https://doi.org/10.14569/IJACSA.2023.0140224

    Article  Google Scholar 

  18. Ramezani, M, Feizi-Derakhshi, MR, Balafar, MA (2022) Knowledge graph-enabled text-based automatic personality prediction. Comput Intell Neurosci, 2022. https://doi.org/10.1155/2022/3732351

  19. Kim, KY, Yang, YB, Kim, MR, Park, JS, Kim, J (2023) Mbti Personality Type Prediction Model Using Wzt Analysis Based On The Cnn Ensemble And Gan. Hum-Centric Comput Inf Sci, 13. https://doi.org/10.22967/Hcis.2023.13.014

  20. Quwaider M, Alabed A, Duwairi R (2023) Shooter video games for personality prediction using five factor model traits and machine learning. Simul Model Pract Theory 122:102665. https://doi.org/10.1016/j.simpat.2022.102665

    Article  Google Scholar 

  21. Yang K, Lau RYK, Abbasi A (2023) Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality. Inf Syst Res 34(1):194–222. https://doi.org/10.1287/isre.2022.1111

    Article  Google Scholar 

  22. Maqsood R, Ceravolo P, Romero C, Ventura S (2022) Modeling and predicting students’ engagement behaviors using mixture Markov models. Knowl Inf Syst 64(5):1349–1384

    Article  Google Scholar 

  23. Vijayvergia A, Kumar K (2021) Selective shallow models strength integration for emotion detection using GloVe and LSTM. Multimed Tools Appl 80(18):28349–28363

    Article  Google Scholar 

  24. Asghar MZ, Lajis A, Alam MM, Rahmat MK, Nasir HM, Ahmad H, Al-Rakhami MS, Al-Amri A, Albogamy FR (2022) A deep neural network model for the detection and classification of emotions from textual content. Complexity 2022:1–12. https://doi.org/10.1155/2022/8221121

    Article  ADS  Google Scholar 

  25. Pedregosa F, Varoquaux G, Gramfort A et al (2011) sklearn. Model selection. Train-Test-Split J Mach Learn Res 12:2825–2830

    Google Scholar 

  26. Shah, T (2017) About Train, Validation and Test Sets in Machine Learning. Retrieved from https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7

  27. Jillani N, Khattak AM, Asghar MZ, Ullah H (2023) Efficient diagnosis of liver disease using deep learning technique. In 2023 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, Jeju, Republic of Korea, pp 1–6. https://doi.org/10.1109/MeMeA57477.2023.10171906

  28. Xu Y, Goodacre R (2018) On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning. J Anal Test 2(3):249–262

    Article  PubMed  PubMed Central  Google Scholar 

  29. Koppanati RK, Kumar K (2020) P-MEC: polynomial congruence-based multimedia encryption technique over cloud. IEEE Consum Electron Mag 10(5):41–46

    Article  Google Scholar 

  30. Vijayvergia A, Kumar K (2018) STAR: rating of reviews by exploiting variation in emotions using transfer learning framework. 2018 Conference on Information and Communication Technology (CICT)

  31. Sharma S, Shivhare SN, Singh N, Kumar K (2019) Computationally efficient ann model for small-scale problems. In Machine intelligence and signal analysis (pp. 423–435). Springer, Singapore

    Google Scholar 

  32. Wong KF, Franklin M (1996) Checkpointing in distributed computing systems. J Parallel Distrib Comput 35(1):67–75. https://doi.org/10.1006/jpdc.1996.0069

    Article  Google Scholar 

Download references

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.

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nosheen Jellani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Zubair Asghar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Usama Asghar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Zubair Asghar.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors and does not contain any studies with animals performed by any of the authors.

Conflict of Interest

Authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation