Abstract
Automated personality traits prediction from widely available social media text data, is finding its increased applications in recommender systems, psychology, forecasting, and decision making. The aim of this research is to break the digital text data into features, analyse and map it to an appropriate personality model. Because of its simplicity and shown competence, a well-known personality model known as the Big Five personality characteristics has frequently been welcomed in the literature as the norm for personality evaluation. Recent advances in automated personality detection have focused on including sentiments, emotions, linguistic styles, and other natural language processing techniques. All these approaches are proposed by a fact concerned with the limited amount of data available for processing by deep learning algorithms. Personality datasets with conventional personality labels are few, and collecting them is challenging due to privacy concerns, as well as the high expense of hiring expert psychologists to label them. The performance of the model can even be increased if a large amount of labelled data is available. This research proposes a new personality prediction model using data source mapping and data fusion techniques. The results are evident that the proposed methodology has outperformed the existing methodologies. To be more precise, the results had the highest accuracy of 87.89% and 0.924 F1 measure score after mapping MBTI into Big Five personality traits and later, fusion with Essays and myPersonality datasets.
Similar content being viewed by others
Data availability
The datasets for this study are available on request to the corresponding author.
Change history
27 April 2024
A Correction to this paper has been published: https://doi.org/10.1007/s00521-024-09651-9
References
Vinciarelli A, Mohammadi G (2014) A survey of personality computing
Costa PT, McCrae RR (1998) Trait theories of personality. In: Barone DF, Hersen M, van Hasselt VB (eds) Advanced personality. The plenum series in social/clinical psychology. Springer, Boston, pp 103–121
McCrae RR, John OP (1992) An introduction to the five-factor model and its applications. J Pers 60:175–215. https://doi.org/10.1111/j.1467-6494.1992.tb00970.x
Furnham AJP, Differences I (1996) The big five versus the big four: the relationship between the Myers–Briggs type indicator (MBTI) and NEOPI five factor model of personality. Pers Individ Differ 21:303–307. https://doi.org/10.1016/0191-8869(96)00033-5
Cattell HE, Mead AD (2008) The sixteen personality factor questionnaire (16PF)
Alam F, Stepanov EA, Riccardi G (2013) Personality traits recognition on social network—Facebook. AAAI Workshop, Technical Report, WS-13-01, pp 6–9
Dalvi-Esfahani M, Niknafs A, Alaedini Z, Barati Ahmadabadi H, Kuss DJ, Ramayah T (2020) Social media addiction and empathy: moderating impact of personality traits among high school students. Telemat Inform. https://doi.org/10.1016/j.tele.2020.101516
Han S, Huang H, Tang Y (2020) Knowledge of words: an interpretable approach for personality recognition from social media. Knowl Based Syst 194:105550. https://doi.org/10.1016/j.knosys.2020.105550
Howlader P, Pal KK, Cuzzocrea A, Kumar SDM (2018) Predicting facebook-users’ personality based on status and linguistic features via fexible regression analysis techniques. Proc ACM Sympos Appl Comput. https://doi.org/10.1145/3167132.3167166
Khurana D, Koli A, Khatter K, Singh S (2018) Natural language processing : state of the art , current trends and challenges natural language processing: state of the art, current trends and challenges Department of Computer Science and Engineering Manav Rachna International University, Faridabad. ArXiv Preprint ArXiv, August 2017
Kircaburun K, Alhabash S, Tosuntaş ŞB, Grifths MD (2020) Uses and gratifcations of problematic social media use among university students: a simultaneous examination of the big five of personality traits, social media platforms, and social media use motives. Int J Ment Health Addict 18(3):525–547. https://doi.org/10.1007/s11469-018-9940-6
Crayne MP, Medeiros KE (2020) Making sense of crisis: charismatic, ideological, and pragmatic leadership in response to Covid-19. Am Psychol 76(3):462–474
Guest JL, Rio CD, Sanchez T (2020) The three steps needed to end the Covid-19 pandemic: bold public health leadership, rapid innovations, and courageous political will. JMIR Public Health 6(2):e19043
Nadkarni S, Herrmann POL (2010) CEO personality, strategic flexibility, and firm performance: the case of the Indian business process outsourcing industry. Acad Manag J 53(5):1050–1073
Riaz MN, Riaz MA, Batool N (2012) Personality types as predictors of decision making styles. J Behav Sci 22(2):99–114
Judge TA, Piccolo RF, Kosalka T (2009) The bright and dark sides of leader traits: a review and theoretical extension of the leader trait paradigm. Leadersh Quart 20(6):855–875
Judge TA, Bono JE, Ilies R, Gerhardt MW (2002) Personality and leadership: a qualitative and quantitative review. J Appl Psychol 87(4):765–780
Peterson RS, Smith DB, Martorana PV, Owens PD (2003) The impact of chief executive officer personality on top management team dynamics: one mechanism by which leadership affects organizational performance. J Appl Psychol 88(5):795–808
LePine JA, Van Dyne L (2001) Voice and cooperative behavior as contrasting forms of contextual performance: evidence of differential relationships with big five personality characteristics and cognitive ability. J Appl Psychol 86(2):326
Celli F, Pianesi F, Stillwell D, Kosinski M, et al (2013) Workshop on computational personality recognition (shared task). In: Proceedings of 7th international AAAI conference on weblogs and social media (AAAI, California), pp 2–5
Farnadi G, Zoghbi S, Moens MF, Cock MD (2013) Recognising personality traits using Facebook status updates. In: Proceedings of 7th international AAAI conference on weblogs and social media (AAAI, California), pp 14–18
Adamopoulos P, Ghose A, Todri V (2018) The impact of user personality traits on word of mouth: text-mining social media platforms. Inf Syst Res 29(3):612–640
Pratama BY, Sarno R (2015) Personality classification based on Twitter text using naive Bayes, KNN and SVM. In: Proceedings of IEEE international conference on data and software engineering (IEEE, New York), pp 170–174
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
Majumder N, Poria S, Gelbukh A, Cambria E (2017) Deep learningbased document modeling for personality detection from text. IEEE Intell Syst 32(2):74–79
Yu J, Markov K (2017) Deep learning based personality recognition from Facebook status updates. In: Proceedings of 8th IEEE international conference on awareness sciences and technology (IEEE, New York), pp 383–387
Xue D, Wu L, Hong Z et al (2018) Deep learning-based personality recognition from text posts of online social networks. Appl Intell 48:4232–4246
Prantik H et al (2018) Predicting facebook-users’ personality based on status and linguistic features via flexible regression analysis techniques. In: Proceedings of the 33rd annual ACM symposium on applied computing
Pennebaker JW, Francis ME, Booth RJ (2001) Linguistic inquiry and word count: LIWC 2001. Lawrence Erlbaum Associates, Mahway
Bazelli B, Hindle A, Stroulia E (2013) On the personality traits of StackOverflow users. In: 2013 IEEE international conference on software maintenance, pp 460–463
Golbeck JA (2016) Predicting personality from social media text. AIS Trans Replic Res 2:1–10. https://doi.org/10.17705/1atrr.00009
Majumder N, Poria S, Gelbukh A, Cambria E (2017) Deep learning based document modeling for personality detection from text. IEEE Intell Syst 32:74–79. https://doi.org/10.1109/MIS.2017.23
Hernandez R, Scott I (2017) Predicting Myers–Briggs type indicator with text. In: 31st Conference on neural information processing systems (NIPS), pp 4–9
Xue D, Wu L, Hong Z, Guo S, Gao L, Wu Z et al (2018) Deep learning-based personality recognition from text posts of online social networks. Appl Intell 48:4232–4246. https://doi.org/10.1007/s10489-018-1212-4
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). IEEE, pp 1–6
Mehta Y, Fatehi S, Kazameini A, Stachl C, Cambria E, Eetemadi S (2020a) Bottom-up and top-down: predicting personality with psycholinguistic and language model features. In: 2020 IEEE international conference on data mining (ICDM), pp 1184–1189
Ren Z, Shen Q, Diao X, Xu H (2021) A sentiment-aware deep learning approach for personality detection from text. Inf Process Manag 58:102532. https://doi.org/10.1016/j.ipm.2021.102532
Vilares D, Peng H, Satapathy R, Cambria E(2018) BabelSenticNet: a commonsense reasoning framework for multilingual sentiment analysis. In: 2018 IEEE symposium series on computational intelligence (SSCI), pp 1292–1298
Elmitwally N (2022) Personality detection using context based emotions in cognitive agents. CMC Comput Mater Continua 70(3):4947–4964. https://doi.org/10.32604/cmc.2022.021104
El-Demerdash K, El-Khoribi RA, Shoman MAI, Abdou S (2022) Deep learning based fusion strategies for personality prediction. Egypt Inform J 23(1):47–53. https://doi.org/10.1016/j.eij.2021.05.004
Kerz E, Qiao Y (2022) Pushing on personality detection from verbal behavior: a transformer meets text contours of psycholinguistic features. http://arxiv.org/abs/2204.04629v1 [cs.CL]. https://doi.org/10.48550/arXiv.2204.04629
Zhu Y, Hu L, Ning N, Zhang W, Wu B (2022) A lexical psycholinguistic knowledge-guided graph neural network for interpretable personality detection. Knowl Based Syst 249:108952. https://doi.org/10.1016/j.knosys.2022.108952
Yang T, Deng J (2022) Orders are unwanted: dynamic deep graph convolutional network for personality detection. http://arxiv.org/abs/2212.01515v2 [cs.CL]. https://doi.org/10.48550/arXiv.2212.01515
Pennebaker J, King LA (1999) Linguistic styles: language use as an individual difference. J Person Soc Psychol 77(6):1296–1312
Furnham A (1996) The big five versus the big four: the relationship between the Myers–Briggs type indicator and the NEO-PI five-factor model of personality. Pers Individ Differ 2:303–307
Furnham A, Moutafi J, Crump J (2003) The relationship between the revised NEO-personality inventory and the Myers–Briggs type indicator. Soc Behav Pers 6:577–584
McCrae RR, Costa PT Jr (1989) Reinterpreting the Myers–Briggs type indicator from the perspective of the five-factor model of personality. J Pers 1:17–40
Zheng H, Wu C (2019) Predicting personality using Facebook status based on semi-supervised learning. ACM Int Conf Proc Ser. https://doi.org/10.1145/3318299.3318363
Rashinkar P, Krushnasamy VS (2017) An overview of data fusion techniques. In: International conference on innovative mechanisms for industry applications (ICIMIA), pp 694–697. https://doi.org/10.1109/ICIMIA.2017.7975553.
Mehta Y, Majumder N, Gelbukh A, Cambria E (2019) Recent trends in deep learning based personality detection. Artif Intell Rev. https://doi.org/10.1007/s10462-019-09770-z
Du K-L, Swamy MNS (2019) Combining multiple learners: data fusion and ensemble learning. In: Neural networks and statistical learning. Springer, London
Devlin J, Chang M-W, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. https://doi.org/10.48550/arXiv.1810.04805
Peters ME, Neumann M, Zettlemoyer L, Yih WT (2020) Dissecting contextual word embeddings: architecture and representation. In: Proceedings of the 2018 conference on empirical methods in natural language processing, EMNLP 2018, pp 1499–1509. https://doi.org/10.18653/v1/d18-1179
Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Nat Acad Sci 110(15):5802–5805. https://doi.org/10.1073/pnas.1218772110
Tandera T, Hendro S, Suhartono D, Wongso R, Prasetio YL (2017) Personality prediction system from Facebook users. Procedia Comput Sci 116:604–611
Yuan C, Wu J, Li H, Wang L (2018) Personality recognition based on user generated content. In: 15th International conference on service systems and service management ICSSSM (IEEE), pp 1–6
Christian H, Suhartono D, Chowanda A, Zamli KZ (2021) Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging. J Big Data 1:1–20
Funding
No funding received.
Author information
Authors and Affiliations
Contributions
SJJ contributed to conceptualization, methodology, software, data curation, writing—original draft, writing—review and editing. MRM contributed to visualization, investigation, supervision.
Corresponding author
Ethics declarations
Conflict of interest
The authors certify that there is no conflict of interest in the subject matter discussed in this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this article was revised to correct the Authors affiliation and first Author email address.
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.
About this article
Cite this article
Sirasapalli, J.J., Malla, R.M. A deep learning approach to text-based personality prediction using multiple data sources mapping. Neural Comput & Applic 35, 20619–20630 (2023). https://doi.org/10.1007/s00521-023-08846-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-023-08846-w