[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Automatic personality prediction: an enhanced method using ensemble modeling

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. For more attention by computational researchers.

  2. A more complete list is provided at the beginning of Sect. 2.

References

  1. Corr PJ, Matthews G (2009) The cambridge handbook of personality psychology.Cambridge handbooks in psychology. Cambridge University Press, Cambridge

    Book  Google Scholar 

  2. Kazdin Alan E (2000) Encyclopedia of psychology. American Psychological Association, Washington

    Google Scholar 

  3. Aydin B, Kindiroglu AA, Aran O, Akarun L (2016) Automatic personality prediction from audiovisual data using random forest regression. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 37–42

  4. Mohammadi G, Vinciarelli G (2015) Automatic personality perception: Prediction of trait attribution based on prosodic features extended abstract. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pages 484–490

  5. Huan Z, Zeying Y, Zuo C, and Xixiang Z (2015) Automatic chinese personality recognition based on prosodic features. In Xiangjian He, Suhuai Luo, Dacheng Tao, Changsheng Xu, Jie Yang, and Muhammad Abul Hasan, editors, MultiMedia Modeling, pages 180–190, Cham, . Springer International Publishing

  6. Jothilakshmi S, Sangeetha J, Brindha R (2017) Speech based automatic personality perception using spectral features. Int J Speech Technol 20(1):43–50

    Article  Google Scholar 

  7. Xu J, Tian W, Fan Y, Lin Y, Zhang C (2018) Personality trait prediction based on 2.5d face feature model. In: Xingming S, Zhaoqing P, Elisa B (eds) Cloud Computing and Security. Springer International Publishing, Cham

    Google Scholar 

  8. Celli F, Bruni E, Lepri B(2014) Automatic personality and interaction style recognition from facebook profile pictures. In Proceedings of the 22Nd ACM International Conference on Multimedia, MM ’14, pages 1101–1104, New York, NY, USA, ACM

  9. Ferwerda B, Schedl M, Tkalcic M (2016) Using instagram picture features to predict users’ personality. In: Qi T, Nicu S, Guo-Jun Q, Benoit H, Richang H, Xueliang L (eds) MultiMedia modeling. Springer International Publishing, Cham

    Google Scholar 

  10. Vo Nhi NY, Liu S, He X, Xu G (2018) Multimodal mixture density boosting network for personality mining. In: Dinh P, Vincent ST, Geoffrey IW, Bao H, Mohadeseh G, Lida R (eds) Advances in knowledge discovery and data mining. Springer International Publishing, Cham, pp 644–655

    Google Scholar 

  11. Güçlütürk Y, Güçlü U, Pérez M, Escalante HJ, Baró X, Andujar C, Guyon I, Junior JJ, Madadi M, Escalera S, Van Gerven MAJ, Van Lier R(2017) Visualizing apparent personality analysis with deep residual networks. In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pages 3101–3109

  12. Zhang C-L, Zhang H, Wei X-S, Wu J (2016) Deep bimodal regression for apparent personality analysis. In Computer Vision – ECCV 2016 Workshops, pages 311–324, Cham. Springer International Publishing

  13. Hima P, Shanmugam M (2019) Big-five personality traits based on four main methods. In: Santosh KC, Hegadi Ravindra S (eds) Recent trends in image processing and pattern recognition. Springer Singapore, Singapore, pp 726–734

    Chapter  Google Scholar 

  14. Chen T-Y, Tsai M-C, Chen Y-M (2016) A user’s personality prediction approach by mining network interaction behaviors on facebook. Online Inf Rev 40(7):913–937

    Article  Google Scholar 

  15. Tadesse MM, Lin H, Xu B, Yang L (2018) Personality predictions based on user behavior on the facebook social media platform. IEEE Access 6:61959–61969

    Article  Google Scholar 

  16. Shen J, Brdiczka O, Liu J (2013) Understanding email writers: Personality prediction from email messages. In: Sandra C, Stephan W, Alessandro M, Giovanni S (eds) User modeling, adaptation, and personalization. Springer, Berlin

    Google Scholar 

  17. Lemos N, Shah K, Rade R, Shah D (2018) Personality prediction based on handwriting using machine learning. In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), pages 110–113

  18. Wijaya W, Tolle H, Utaminingrum F (2018) Personality analysis through handwriting detection using android based mobile device. J Inf Technol Computer Sci 2(2):871

    Google Scholar 

  19. Küster L, Trahms C, Voigt-Antons J (2018) Predicting personality traits from touchscreen based interactions. In 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), pages 1–6

  20. Lokhande VR, Gawali BW (2017) Analysis of signature for the prediction of personality traits. In 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), pages 44–49

  21. Celiktutan O, Sariyanidi E, Gunes H (2015) Let me tell you about your personality!: Real-time personality prediction from nonverbal behavioural cues. In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), volume 1, pages 1–1

  22. Yakoub F, Zein M, Yasser K, Adl A, Hassanien AE (2015) Predicting personality traits and social context based on mining the smartphones sms data. In: Ajith A, Xin Hua J, Václav S, Jeng-Shyang P (eds) Intelligent data analysis and applications. Springer International Publishing, Cham

    Google Scholar 

  23. Kotu V, Deshpande B (2018) Data science: concepts and practice. Morgan Kaufmann, USA

    Google Scholar 

  24. Allport G W (1937) Personality: A psychological interpretation

  25. Cattell Raymond B, Eber Herbert W, Tatsuoka Maurice M (1970) Handbook for the sixteen personality factor questionnaire (16 PF): In clinical, educational, industrial, and research psychology, for use with all forms of the test. Institute for Personality and Ability Testing

  26. Eysenck HJ (1950) Dimensions of personality

  27. Myers IB, Briggs KC, McCaulley MH (1976) Myers-briggs type indicator. Consulting Psychologists Press, Form F and Form G

  28. McCrae Robert R, John Oliver P (1992) An introduction to the five-factor model and its applications. J Personality 60(2):175–215

    Article  Google Scholar 

  29. Matz S, Chan Yin WF, Kosinski M (2016) Models of personality. Springer International Publishing, Cham

    Google Scholar 

  30. Goldberg Lewis R (1993) The structure of phenotypic personality traits. Am Psychol 48(1):26

    Article  Google Scholar 

  31. Uher J (2013) Personality psychology: lexical approaches, assessment methods, and trait concepts reveal only half of the story–why it is time for a paradigm shift. Integr Psychol Behav Sci 47(1):1–55

    Article  Google Scholar 

  32. McCrae Robert R, Terracciano A (2005) Universal features of personality traits from the observer’s perspective: data from 50 cultures. J Personality Soc Psychol 88(3):547

    Article  Google Scholar 

  33. John Oliver P, Srivastava S et al (1999) The big five trait taxonomy: history, measurement, and theoretical perspectives. Handbook of Personality: Theor Res 2:102–138

    Google Scholar 

  34. Cherry K (2020) The big five personality traits. https://www.verywellmind.com/the-big-five-personality-dimensions-2795422

  35. Re B, Ewen RB (2014) An introduction to theories of personality, 7th edn. Taylor & Francis, New York

    Google Scholar 

  36. Ashton Michael C (2013) Individual differences and personality. Academic Press, Cambridge

    Google Scholar 

  37. Barlett CP, Anderson CA (2012) Direct and indirect relations between the big 5 personality traits and aggressive and violent behavior. Personality Individual Diff 52(8):870–875

    Article  Google Scholar 

  38. Le Corff Y, Toupin J (2010) The five-factor model of personality at the facet level: association with antisocial personality disorder symptoms and prediction of antisocial behavior. J Psychopathol Behav Assess 32(4):586–594

    Article  Google Scholar 

  39. Yun M, Kim E, Park W-S (2017) A test of an integrative model using social factors and personality traits: Prediction on the delinquency of south Korean youth. Int J Offender Therap Comparative Criminol 61(11):1262–1287

    Article  Google Scholar 

  40. Reardon Kathleen W, Wang M, Neighbors C, Tackett JL (2019) The personality context of adolescent gambling: Better explained by the big five or sensation-seeking? J Psychopathol Behav Assess 41(1):69–80

    Article  Google Scholar 

  41. Rosenström T, Torvik Fartein A, Ystrom E, Czajkowski NO, Gillespie Nathan A, Aggen Steven H, Krueger Robert F, Kendler Kenneth S, Reichborn-Kjennerud T (2018) Prediction of alcohol use disorder using personality disorder traits: a twin study. Addiction 113(1):15–24

    Article  Google Scholar 

  42. Pruysers S, Blais J, Chen PG (2019) Who makes a good citizen? the role of personality. Personality Individual Diff 146:99–104

    Article  Google Scholar 

  43. Barańczuk U (2019) The five factor model of personality and emotion regulation: a meta-analysis. Personality and Individual Diff 139:217–227

    Article  Google Scholar 

  44. Mohammadi E, Bagheri M, Asgarizadeh G (2018) The role of perceived social support and aspects of personality in the prediction of marital instability: The mediating role of occupational stress. Int J Psychol (IPA) 12(1):162–185

    Article  Google Scholar 

  45. Chen T-H, Ho R-J, Liu Y-W (2018) Investor personality predicts investment performance? a statistics and machine learning model investigation. Computers Human Behav 65:871

    Google Scholar 

  46. Liu Z, Yi W, Mahmud J, Akkiraju R, Schoudt J, Xu A, Donovan B (2016) To buy or not to buy? understanding the role of personality traits in predicting consumer behaviors. In: Emma S, Yong-Yeol A (eds) Social informatics. Springer International Publishing, Cham

    Google Scholar 

  47. Muller J, Schwieren C (2019) Big five personality factors in the trust game. J Business Econ 87:641

    Google Scholar 

  48. Leutner F, Ahmetoglu G, Akhtar R, Chamorro-Premuzic T (2014) The relationship between the entrepreneurial personality and the big five personality traits. Personality and Individual Diff 63:58–63

    Article  Google Scholar 

  49. Kim HJ, Shin KH, Swanger N (2009) Burnout and engagement: a comparative analysis using the big five personality dimensions. Int J Hospitality Manage 28(1):96–104

    Article  Google Scholar 

  50. Sood A, Bhatia R(2016) Ensemble recommendation for employability using cattell’s sixteen personality factor model. In Suresh CS and Swagatam D (eds)Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2, pages 23–31, Cham. Springer International Publishing

  51. Mendes FF, Mendes E, Salleh N (2019) The relationship between personality and decision-making: a systematic literature review. Inf Softw Technol 111:50–71

    Article  Google Scholar 

  52. Vecchione Michele, Schoen Harald, José Luis González Castro, Jan Cieciuch, Vassilis Pavlopoulos, and Gian Vittorio Caprara (2011) Personality correlates of party preference: The big five in five big european countries. Personality and Individual Differences, 51(6):737 – 742

  53. Jonason PK (2014) Personality and politics. Personality and Individual Diff 71:181–184

    Article  Google Scholar 

  54. Abe Jo AA (2018) Personality and political preferences: The 2016 U.S. presidential election. J Res Personality 77:70–82

    Article  Google Scholar 

  55. Higgins Daniel M, Peterson Jordan B, Pihl Robert O, Lee Alice GM (2007) Prefrontal cognitive ability, intelligence, big five personality, and the prediction of advanced academic and workplace performance. J Personality Soc Psychol 93(2):298

    Article  Google Scholar 

  56. Komarraju M, Karau Steven J, Schmeck Ronald R (2009) Role of the big five personality traits in predicting college students academic motivation and achievement. Learn Individual Diff 19(1):47–52

    Article  Google Scholar 

  57. Pozzebon Julie A, Ashton Michael C, Visser Beth A (2014) Major changes: personality, ability, and congruence in the prediction of academic outcomes. J Career Assess 22(1):75–88

    Article  Google Scholar 

  58. Marcela V (2015) Learning strategy, personality traits and academic achievement of university students. Procedia - Social and Behavioral Sciences, 174:3473 – 3478. International Conference on New Horizons in Education, INTE 2014, 25-27 June 2014, Paris, France

  59. Sorić I, Penezić Z, Burić I (2017) The big five personality traits, goal orientations, and academic achievement. Learn Individual Diff 54:126–134

    Article  Google Scholar 

  60. Reisz Z, Boudreaux Michael J, Ozer Daniel J (2013) Personality traits and the prediction of personal goals. Personality and Individual Diff 55(6):699–704

    Article  Google Scholar 

  61. Walker Christopher O (2017) Exploring the contributions of grit and personality in the prediction of self-and other-forgiveness. J Individual Diff 65:81

    Google Scholar 

  62. Anglim J, Grant Sh (2016) Predicting psychological and subjective well-being from personality: incremental prediction from 30 facets over the big 5. J Happiness Stud 17(1):59–80

    Article  Google Scholar 

  63. Montag C, Elhai Jon D (2019) A new agenda for personality psychology in the digital age? Personality Individual Diff 147:128–134

    Article  Google Scholar 

  64. Wright William R, Chin David N (2014) Personality profiling from text: Introducing part-of-speech n-grams. In: Vania D, Tsvi K, David C, Francesco R, Peter D, Geert-Jan H (eds) User modeling, adaptation, and personalization. Springer International Publishing, Cham, pp 243–253

    Chapter  Google Scholar 

  65. Arjaria S, Shrivastav A, Rathore AS, Tiwari V (2019) Personality trait identification for written texts using MLNB. Springer Singapore, Singapore

    Book  Google Scholar 

  66. Sun X, Liu B, Cao J, Luo J, Shen X (2018) Who am i? personality detection based on deep learning for texts. In 2018 IEEE International Conference on Communications (ICC), pages 1–6

  67. Xue D, Lifa W, Hong Z, Guo S, Gao L, Zhiyong W, Zhong X, Sun J (2018) Deep learning-based personality recognition from text posts of online social networks. Appl Intell 48(11):4232–4246

    Article  Google Scholar 

  68. Garcia V (2017). In: Kamil E, Václav M (eds) Text, speech, and dialogue. Springer International Publishing, Cham

  69. Dandannavar PS, Mangalwede SR, Kulkarni PM (2018) Social media text - a source for personality prediction. In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), pages 62–65

  70. Buettner R (2017) Predicting user behavior in electronic markets based on personality-mining in large online social networks. Electron Markets 27(3):247–265

    Article  Google Scholar 

  71. Azucar D, Marengo D, Settanni M (2018) Predicting the big 5 personality traits from digital footprints on social media: A meta-analysis. Personality Individual Diff 124:150–159

    Article  Google Scholar 

  72. Levitan SI, Levitan Y, An G, Levine M, Levitan R, Rosenberg A, Hirschberg J(2016) Identifying individual differences in gender, ethnicity, and personality from dialogue for deception detection. In Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pages 40–44, San Diego, California. Association for Computational Linguistics

  73. Dave A (2016) Application of convolutional neural network models for personality prediction from social media images and citation prediction for academic papers. PhD thesis, UC San Diego

  74. Alireza S, Shafigheh H, Amir Masoud R (2018) Personality classification based on profiles of social networks’ users and the five-factor model of personality. Human-centric Comput Inf Sci 8(1):24

    Article  Google Scholar 

  75. Graciela G, Elvis S, Freddy T (2019) Predict the personality of facebook profiles using automatic learning techniques and bfi test. In: Álvaro R, Hojjat A, Luís PR, Sandra C (eds) New knowledge in information systems and technologies. Springer International Publishing, Cham, pp 482–493

    Google Scholar 

  76. Laleh A, Shahram R (2017) Analyzing facebook activities for personality recognition. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 960–964

  77. Ong V, Rahmanto ADS, Williem, Suhartono D, Nugroho AE, Andangsari EW, Suprayogi MN (2017) Personality prediction based on twitter information in bahasa indonesia. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 367–372

  78. Gavrilescu M, Vizireanu N (2018) Predicting the big five personality traits from handwriting. EURASIP J Image and Video Process 1:57

    Article  Google Scholar 

  79. Fabrizio N, Alexis H, Jonas B (2017) Generation of virtual characters from personality traits. In: Iolanda L, Stefan K, Christopher P, Ginevra C (eds) Carol intelligent virtual agents. Springer International Publishing, Cham

  80. Vernon Richard JW, Sutherland Clare AM, Young Andrew W, Hartley T (2014) Modeling first impressions from highly variable facial images. Proceedings of the National Academy of Sciences, 111(32):E3353–E3361

  81. Abdulrahman R, Alsaedi R, AlSobeihy M (2018) Automated student-to-major allocation based on personality prediction. In 2018 1st International Conference on Computer Applications Information Security (ICCAIS), pages 1–6

  82. Wu W, Chen L (2015) Implicit acquisition of user personality for augmenting movie recommendations. In: Francesco R, Kalina B, Owen C, Séamus L (eds) User modeling, adaptation and personalization. Springer International Publishing, Cham

    Google Scholar 

  83. Pennebaker James W, King Laura A (1999) Linguistic styles: language use as an individual difference. J Personality Soc Psychol 77(6):1296

    Article  Google Scholar 

  84. Manning C, Raghavan P, Suchutze H (2010) Introduction to information retrieval. Nat Language Eng 16(1):100–103

    Google Scholar 

  85. Gruber T (2009) Ontology, pages 1963–1965. Springer US, Boston, MA

  86. Fellbaum C (2005) Wordnet and wordnets. pages 665–670

  87. Yarkoni T (2010) Personality in 100,000 words: a large-scale analysis of personality and word use among bloggers. J Res Personality 44(3):363–373

    Article  Google Scholar 

  88. Landauer Thomas K, Foltz Peter W, Laham D (1998) An introduction to latent semantic analysis. Discourse Process 25(2–3):259–284

    Article  Google Scholar 

  89. Landauer TK, McNamara DS, Dennis S, Kintsch W (2013) Handbook of latent semantic analysis. Taylor & Francis, Cambridge

    Google Scholar 

  90. van Rijn Jan N, Holmes G, Pfahringer B, Vanschoren J (2018) The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach Learn 107(1):149–176

    Article  MathSciNet  MATH  Google Scholar 

  91. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    Article  MATH  Google Scholar 

  92. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Computer Syst Sci 55(1):119–139

    Article  MathSciNet  MATH  Google Scholar 

  93. David H (1992) Stacked generalization. Neural Net 5(2):241–259

    Article  Google Scholar 

  94. Douibi K, Settouti N, El Amine CM (2017) The homogeneous ensemble methods for mlknn algorithm. In Proceedings of the International Conference on Compute and Data Analysis, ICCDA ’17, page 197-201, New York, NY, USA. Association for Computing Machinery

  95. Kang S, Cho S, Kang P (2015) Multi-class classification via heterogeneous ensemble of one-class classifiers. Eng Appl Artif Intell 43:35–43

    Article  Google Scholar 

  96. van Jan R, Geoffrey H, Bernhard P, Joaquin V (2018) The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach Learn 107(1):149–176

    Article  MathSciNet  MATH  Google Scholar 

  97. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E(2016) Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1480–1489, San Diego, California. Association for Computational Linguistics

  98. Yelong S, Xiaodong H, Jianfeng G, Li D, and Grégoire M (2014) A latent semantic model with convolutional-pooling structure for information retrieval. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM ’14, page 101-110, New York, NY, USA. Association for Computing Machinery

  99. David JH, Tom B, Paul I (2018) The wiley handbook of psychometric testing: a multidisciplinary reference on survey, scale and test development. Wiley-Blackwell, New Jersey

    Google Scholar 

  100. Tighe Edward P, Ureta Jennifer C (2016) Bernard Andrei L. Pollo, Charibeth K. Cheng, and Remedios de Dios Bulos. Personality trait classification of essays with the application of feature reduction. In SAAIP@IJCAI

  101. Salminen J, Rao Rohan G, Jung S-G, Chowdhury Shammur A, Jansen Bernard J (2020) Enriching social media personas with personality traits: A deep learning approach using the big five classes. In: Helmut D, Lauren R-J (eds) Artificial intelligence in HCI. Springer International Publishing, Cham

    Google Scholar 

  102. 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

    Article  Google Scholar 

  103. Kazameini A, Fatehi S, Mehta Y, Eetemadi S, Cambria E (2020) Personality trait detection using bagged svm over bert word embedding ensembles

Download references

Acknowledgements

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

Funding

This study was funded by the University of Tabriz (Grant Number S/806).

Author information

Authors and Affiliations

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

    Majid Ramezani, Mohammad-Reza Feizi-Derakhshi, Meysam Asgari-Chenaghlu, Ali-Reza Feizi-Derakhshi, Narjes Nikzad-Khasmakhi, Mehrdad Ranjbar-Khadivi, Zoleikha Jahanbakhsh-Nagadeh, Elnaz Zafarani-Moattar & Taymaz Akan

  2. Department of Computer Engineering, University of Tabriz, Tabriz, Iran

    Mohammad-Ali Balafar

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

    Mehrdad Ranjbar-Khadivi

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

    Zoleikha Jahanbakhsh-Nagadeh

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

    Elnaz Zafarani-Moattar

  6. Department of Software Engineering, Istanbul Topkapi University, Istanbul, Turkey

    Taymaz Akan

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

    Taymaz Akan

Authors
  1. Majid Ramezani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad-Reza Feizi-Derakhshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad-Ali Balafar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meysam Asgari-Chenaghlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ali-Reza Feizi-Derakhshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Narjes Nikzad-Khasmakhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mehrdad Ranjbar-Khadivi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zoleikha Jahanbakhsh-Nagadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Elnaz Zafarani-Moattar
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Taymaz Akan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad-Reza Feizi-Derakhshi.

Ethics declarations

Conflict of interest

All of the authors declare that they have no conflict of interest against any company or institution.

Ethical approval

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

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramezani, M., Feizi-Derakhshi, MR., Balafar, MA. et al. Automatic personality prediction: an enhanced method using ensemble modeling. Neural Comput & Applic 34, 18369–18389 (2022). https://doi.org/10.1007/s00521-022-07444-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07444-6

Keywords

Search

Navigation