Skip to main content
SpringerLink
Log in

Personality traits prediction model from Turkish contents with semantic structures

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

Abstract

Users' personality traits can provide different clues about them in the Internet environment. Some areas where these clues can be used are law enforcement, advertising agencies, recruitment processes, and e-commerce applications. In this study, it is aimed to create a dataset and a prediction model for predicting the personality traits of Internet users who produce Turkish content. The main contribution of the study is the personality traits dataset composed of the Turkish Twitter content. In addition, the preprocessing, vectorization, and deep learning model comparisons made in the proposed prediction system will contribute to both current usages and future studies in the relevant literature. It has been observed that the success of the Bidirectional Encoder Representations from Transformers vectorization method and the Stemming preprocessing step on the Turkish personality traits dataset is high. In the previous studies, the effects of these processes on English datasets were reported to have lower success rates. In addition, the results show that the Bidirectional Long Short-Term Memory deep learning method has a better level of success than other methods both for the Turkish dataset and English datasets.

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

Similar content being viewed by others

Data availibility statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Azucar D, Marengo D, Settanni M (2018) Predicting the Big 5 personality traits from digital footprints on social media: a meta-analysis. Personal Individ Differ 124:150–159

    Google Scholar 

  2. Anonymous PAN Shared Tasks. In: Webis. https://pan.webis.de/

  3. Rangel F, Celli F, Rosso P et al (2015) Overview of the 3rd author profiling task at PAN 2015. In: Cappellato L, Ferro N, Jones G, Juan ES (eds) CLEF 2015 evaluation labs and workshop—working notes papers. CEUR-WS.org, Toulouse, France

  4. Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci 110:5802–5805

    Google Scholar 

  5. Ahmad Z, Lutfi SL, Kushan AL et al (2017) Personality prediction of Malaysian Facebook users: cultural preferences and features variation. Adv Sci Lett 23:7900–7903

    Google Scholar 

  6. Laleh A, Shahram R (2017) Analyzing facebook activities for personality recognition. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA), pp 960–964

  7. Tandera T, Hendro SD et al (2017) Personality prediction system from Facebook users. Procedia Comput Sci 116:604–611

    Google Scholar 

  8. Vaidhya M, Shrestha B, Sainju B et al (2017) Personality traits analysis from Facebook data. In: 2017 21st international computer science and engineering conference (ICSEC), pp 1–5

  9. Akhtar R, Winsborough D, Ort U et al (2018) Detecting the dark side of personality using social media status updates. Pers Individ Differ 132:90–97

    Google Scholar 

  10. Hassanein M, Hussein W, Rady S et al (2018) Predicting Personality traits from social media using text semantics. In: 2018 13th international conference on computer engineering and systems (ICCES), pp 184–189

  11. Howlader P, Pal KK, Cuzzocrea A et al (2018) Predicting Facebook-users’ personality based on status and linguistic features via flexible regression analysis techniques. Assoc Computing Machinery, New York

    Google Scholar 

  12. Mao Y, Zhang D, Wu C et al (2018) Feature analysis and optimisation for computational personality recognition. In: 2018 IEEE 4th international conference on computer and communications (ICCC), pp 2410–2414

  13. Tadesse MM, Lin H, Xu B et al (2018) Personality predictions based on user behavior on the Facebook social media platform. IEEE Access 6:61959–61969

    Google Scholar 

  14. Xue D, Wu LF, Hong Z et al (2018) Deep learning-based personality recognition from text posts of online social networks. Appl Intell 48:4232–4246

    Google Scholar 

  15. Marouf AA, Hasan MK, Mahmud H (2019) Identifying neuroticism from user generated content of social media based on psycholinguistic cues. In: 2019 international conference on electrical, computer and communication engineering (ECCE), pp 1–5

  16. Zheng HC, Wu CH, Assoc Comp M (2019) Predicting personality using Facebook status based on semi-supervised learning. Assoc Computing Machinery, New York

    Google Scholar 

  17. Al Marouf A, Hasan MK, Mahmud H (2020) Comparative analysis of feature selection algorithms for computational personality prediction from social media. IEEE Trans Comput Soc Syst 7:587–599

    Google Scholar 

  18. Sun JS, Tian ZQ, Fu YL et al (2020) Digital twins in human understanding: a deep learning-based method to recognize personality traits. Int J Comput Integr Manuf 34:14

    Google Scholar 

  19. Wang S, Cui L, Liu L et al (2020) Personality traits prediction based on users’ digital footprints in social networks via attention RNN. In: 2020 IEEE international conference on services computing (SCC). IEEE, pp 54–56

  20. Zhao JH, Zeng DL, Xiao YJ et al (2020) User personality prediction based on topic preference and sentiment analysis using LSTM model. Pattern Recognit Lett 138:397–402

    Google Scholar 

  21. Başaran S, Ejimogu OH (2021) A neural network approach for predicting personality from Facebook data. SAGE Open 11:21582440211032156

    Google Scholar 

  22. Bakry MR, Nasr MM, Alsheref FK (2022) Personality classification model of social network profiles based on their activities and contents. Int J Adv Comput Sci Appl 13:16–21

    Google Scholar 

  23. Kamalesh MD, Bharathi B (2022) Personality prediction model for social media using machine learning Technique. Comput Electr Eng 100:12

    Google Scholar 

  24. Yang B (2022) Analysis model of personality and psychological characteristics of network users under high-pressure working environment. Secur Commun Netw 2022:10

    Google Scholar 

  25. Zhou LX, Zhang ZY, Zhao LJ et al (2022) Attention-based BiLSTM models for personality recognition from user-generated content. Inf Sci 596:460–471

    Google Scholar 

  26. Ahmad N, Siddique J (2017) Personality assessment using Twitter tweets. In: ZanniMerk C, Frydman C, Toro C, Hicks Y, Howlett RJ, Jain LC (eds) Knowledge-based and intelligent information and engineering systems. Elsevier Science Bv, Amsterdam, pp 1964–1973

    Google Scholar 

  27. Bhatti SK, Muneer A, Lali MI et al (2017) Personality analysis of the USA public using Twitter profile pictures. IEEE, New York

    Google Scholar 

  28. Guntuku SC, Lin WS, Carpenter J et al (2017) Studying personality through the content of posted and liked images on Twitter. Assoc Computing Machinery, New York

    Google Scholar 

  29. Raje MS, Singh A (2018) Personality detection by analysis of Twitter profiles. In: Abraham A, Cherukuri AK, Madureira AM, Muda AK (eds) Proceedings of the eighth international conference on soft computing and pattern recognition. Springer International Publishing Ag, Cham, pp 667–675

  30. Jeremy NH, Prasetyo C, Suhartono D (2019) Identifying personality traits for Indonesian user from Twitter dataset. Int J Fuzzy Log Intell Syst 19:283–289

    Google Scholar 

  31. Tutaysalgir E, Karagoz P, Toroslu IH (2019) Clustering based personality prediction on Turkish tweets. In: 2019 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 825–828

  32. Kosan MA, Karacan H, Urgen BA (2022) Predicting personality traits with semantic structures and LSTM-based neural networks. Alex Eng J 61:8007–8025

    Google Scholar 

  33. Karanatsiou D, Sermpezis P, Gruda D et al (2022) My tweets bring all the traits to the yard: predicting personality and relational traits in online social networks. ACM Trans Web 16:26

    Google Scholar 

  34. Matsumoto K, Kishima R, Tsuchiya S et al (2022) Relationship between personality patterns and harmfulness: analysis and prediction based on sentence embedding. Int J Inf Technol Web Eng 17:24

    Google Scholar 

  35. Rathi S, Verma JP, Jain R et al (2022) Psychometric profiling of individuals using Twitter profiles: a psychological natural language processing based approach. Concurr Comput Pract Exp 34:19

    Google Scholar 

  36. Elbaghazaoui BE, Amnai M, Fakhri Y (2023) Predicting the next word using the Markov chain model according to profiling personality. J Supercomput 16

  37. Ferwerda B, Tkalcic M, Acm, (2018) Predicting users’ personality from Instagram pictures: using visual and/or content features? Assoc Computing Machinery, New York

    Google Scholar 

  38. Kim Y, Kim JH (2018) Using computer vision techniques on Instagram to link users’ personalities and genders to the features of their photos: an exploratory study. Inf Process Manag 54:1101–1114

    Google Scholar 

  39. Huang SG, Zheng JH, Xue D et al (2017) Predicting big-five personality for micro-blog based on robust multi-task learning. In: Zou B, Li M, Wang H, Song X, Xie W, Lu Z (eds) Data science, Pt 1. Springer, Berlin, pp 486–499

    Google Scholar 

  40. Li C, Wan J, Wang B (2017) Personality prediction of social network users. In: 2017 16th international symposium on distributed computing and applications to business, engineering and science (DCABES), pp 84–87

  41. Lin J, Mao W, Zeng DD (2017) Personality-based refinement for sentiment classification in microblog. Knowl-Based Syst 132:204–214

    Google Scholar 

  42. Han SQ, Huang HL, Tang YQ (2020) Knowledge of words: an interpretable approach for personality recognition from social media. Knowl-Based Syst 194:20

    Google Scholar 

  43. Wang P, Yan Y, Si YD et al (2020) Classification of proactive personality: text mining based on Weibo text and short-answer questions text. IEEE Access 8:97370–97382

    Google Scholar 

  44. Wang P, Yan M, Zhan X et al (2021) Predicting self-reported proactive personality classification with Weibo text and short answer text. IEEE Access 9:77203–77211

    Google Scholar 

  45. Jiang Y, Deng S, Li H et al (2021) Predicting user personality with social interactions in Weibo. Aslib J Inf Manag 73(6):839–864

    Google Scholar 

  46. Yang K, Yuan H, Lau RYK (2022) PsyCredit: an interpretable deep learning-based credit assessment approach facilitated by psychometric natural language processing. Expert Syst Appl 198:13

    Google Scholar 

  47. Alsadhan N, Skillicorn D (2017) Estimating personality from social media posts. In: 2017 IEEE international conference on data mining workshops (ICDMW), pp 350–356

  48. Varshney V, Varshney A, Ahmad T et al (2017) Recognising personality traits using social media. In: 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI), pp 2876–2881

  49. Guan Z, Wu B, Wang B et al (2020) Personality2vec: network representation learning for personality. In: 2020 IEEE fifth international conference on data science in cyberspace (DSC). IEEE, pp 30–37

  50. Khan AS, Ahmad H, Asghar MZ et al (2020) Personality classification from online text using machine learning approach. Int J Adv Comput Sci Appl 11:460–476

    Google Scholar 

  51. Sun XG, Liu B, Meng Q et al (2020) Group-level personality detection based on text generated networks. World Wide Web 23:1887–1906

    Google Scholar 

  52. Lopez-Santillan R, Gonzalez LC, Montes-Y-Gomez M et al (2023) When attention is not enough to unveil a text’s author profile: enhancing a transformer with a wide branch. Neural Comput Appl 34:20

    Google Scholar 

  53. Strickland E (2022) Andrew NG: Unbiggen AI. In: IEEE spectrum. https://spectrum.ieee.org/andrew-ng-data-centric-ai

  54. Rammstedt B, John OP (2007) Measuring personality in one minute or less: a 10-item short version of the Big Five Inventory in English and German. J Res Pers 41:203–212

    Google Scholar 

  55. Horzum MB, Tuncay A, Padir MA (2017) Adaptation of big five personality traits scale to Turkish culture. Sakarya Univ J Educ 7:398–408

    Google Scholar 

  56. Gosling SD, Rentfrow PJ, Swann WB Jr (2003) A very brief measure of the Big-Five personality domains. J Res Pers 37:504–528

    Google Scholar 

  57. Atak H (2013) On-Maddeli Kişilik Ölçeği'nin Türk Kültürü'neUyarlanması

  58. Donnellan MB, Oswald FL, Baird BM et al (2006) The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. Psychol Assess 18:192

    Google Scholar 

  59. Korkmaz M, Somer O, Güngör D (2013) Ergen örneklemde beş faktör kişilik envanteri’nin cinsiyetlere göre ortalama ve kovaryans yapılarıyla ölçme eşdeğerliği. Eğitim ve Bilim 38

  60. Soto CJ, John OP (2017) The next Big Five Inventory (BFI-2): developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. J Pers Soc Psychol 113:117

    Google Scholar 

  61. Soto C The Big Five Inventory–2 (BFI-2). In: Colby College—Personality Lab. https://www.colby.edu/psych/personality-lab/#4

  62. Schweter S (2020) BERTurk—BERT models for Turkish. In: Zenodo. https://doi.org/10.5281/zenodo.3770924

  63. Clark K, Luong M-T, Le QV et al (2020) Electra: pre-training text encoders as discriminators rather than generators. arXiv:2003.10555

  64. Bojanowski P, Grave E, Joulin A et al (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146

    Google Scholar 

  65. Grave E, Bojanowski P, Gupta P et al (2018) Learning word vectors for 157 languages. arXiv:1802.06893

  66. Mikolov T, Chen K, Corrado G et al (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781

  67. Lau JH, Baldwin T (2016) An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv:1607.05368

  68. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673–2681

    Google Scholar 

  69. Kumar JA, Abirami S (2021) Ensemble application of bidirectional LSTM and GRU for aspect category detection with imbalanced data. Neural Comput Appl 33:14603–14621

    Google Scholar 

  70. Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12:2451–2471

    Google Scholar 

  71. Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18:602–610

    Google Scholar 

  72. Kiranyaz S, Avci O, Abdeljaber O et al (2021) 1D convolutional neural networks and applications: a survey. Mech Syst Signal Process 151:107398

    Google Scholar 

  73. Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. arXiv:1710.05941

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Software Engineering, Faculty of Engineering and Architecture, Mus Alparslan University, 49250, Mus, Turkey

    Muhammed Ali Kosan

  2. Department of Computer Science, Graduate School of Informatics, Gazi University, 06640, Ankara, Turkey

    Muhammed Ali Kosan

  3. Department of Computer Engineering, Faculty of Engineering, Gazi University, 06570, Ankara, Turkey

    Hacer Karacan

  4. Department of Psychology, Faculty of Economics, Administrative, and Social Sciences, Bilkent University, 06800, Ankara, Turkey

    Burcu A. Urgen

  5. Interdisciplinary Neuroscience Program, Graduate School of Engineering and Science, Bilkent University, 06800, Bilkent, Ankara, Turkey

    Burcu A. Urgen

  6. Aysel Sabuncu Brain Research Center and National Magnetic Resonance Research Center (UMRAM), Ankara, Turkey

    Burcu A. Urgen

Authors
  1. Muhammed Ali Kosan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hacer Karacan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Burcu A. Urgen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MAK and HK helped in conceptualization; MAK worked in methodology; MAK worked in software; MAK, HK, and BAU helped in validation; MAK worked in investigation; MAK worked in resources; MAK and HK helped in data curation; MAK helped in writing—original draft; HK and BAU helped in formal analysis; MAKosan helped in visualization; HK and BAU helped in writing—review and editing; HK and BAU worked in supervision; and HK and BAU worked in project administration.

Corresponding author

Correspondence to Muhammed Ali Kosan.

Ethics declarations

Conflict of interest

All authors declare that there is no conflict of interest.

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

Kosan, M.A., Karacan, H. & Urgen, B.A. Personality traits prediction model from Turkish contents with semantic structures. Neural Comput & Applic 35, 17147–17165 (2023). https://doi.org/10.1007/s00521-023-08603-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08603-z

Keywords

Search

Navigation