Abstract
Psychologists seek to measure personality to analyze the human behavior through a number of methods. As the personality of an individual affects all aspects of a person’s performances, even how he reacts to situations in his social life, academics, job, or personal life. The purpose of this research article is to enlighten the use of personality detection test in an individual’s personal, academics, career or social life, and also provide possible methods to perform personality detection test. One of the possible solutions to detect the personality study is based on the individual’s sense of humor. Throughout the twentieth century, psychologists show an outgoing interest in the study of an individual’s sense of humor. Since individual differences in humor and their relation to psychological well-being can be used to detect the particular personality traits. We have used machine learning used for personality detection that involves the development and initial validation of questionnaire, which assesses four dimensions relating to individual differences in uses of humor. Which are Self-enhancing (humor use to enhance self), Affiliative (humor use to enhance the relationship with other), Aggressive (humor use to enhance the self at the expense of others), and Self-defeating (humor use to enhance relationships at the expense of self).
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References
Eysenek, H.J., Eysenck, H.J.: Dimensions of Personality, vol. 5. Transaction Publishers (1950)
Ruiz-Falcó, A.: /nstituto de Astrofísica de Andalucía, CSIC. Entiempo real Lenguajes de Descripción Hardware: Conceptos y Perspectivas 126, 35 (1997)
Bandura, A., Walters, R. H., Riviere, A.: Aprendizaje social y desarrollo de la personalidad. Alianza Editorial Sa (2007)
De Juan, M.: Personalidad y Criminalidad, Apuntes de Psicología Criminológica. la asignatura Psicología Criminológica, Universidad Autónoma de Madrid (No publicado) (2005)
Francis, L.J., Brown, L.B., Philipchalk, R.: The development of an abbreviated form of the revised Eysenck personality questionnaire (EPQR-A): Its use among students in England, Canada, the USA and Australia. Personality Individ. Differ. 13(4), 443–449 (1992)
Costa, P.T., McCrea, R.R.: Revised neo personality inventory (neo pi-r) and neo five-factor inventory (neo-ffi). Psychological Assessment Resources (1992)
Rossmo, D.K.: Geographic Profiling. CRC press. ISBN-0849381290 (1999)
Caprara, G.V., Barbaranelli, C., Borgogni, L., Perugini, M.: The “Big five questionnaire”: A new questionnaire to assess the five-factor model. Personality Individ. Differ. 15(3), 281–288 (1993)
Reddy, K.H.K., Das, H., Roy, D.S.: A data aware scheme for scheduling big-data applications with SAVANNA Hadoop. Futures of Network. CRC Press, USA (2017)
Mairesse, F., Walker, M.: Automatic recognition of personality in conversation. In: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, pp. 85–88. Association for Computational Linguistics (2006)
Mishra, B.B., Dehuri, S., Panigrahi, B.K., Nayak, A.K., Mishra, B.S.P., Das, H.: Computational intelligence in sensor networks, vol. 776. Studies in computational intelligence. Springer (2018)
Das, H., Jena, A.K., Nayak, J., Naik, B., Behera, H.S.: A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification. In: Computational intelligence in data mining, vol. 2, pp. 461–471. Springer, New Delhi (2015)
Das, H., Jena, A.K., Rath, P.K., Muduli, B., Das, S.R.: Grid computing-based performance analysis of power system: a graph theoretic approach. In: Intelligent computing, communication and devices, pp. 259–266. Springer, New Delhi (2015)
Goldberg, L.R.: Language and individual differences: The search for universals in personality lexicons. Review of personality and social psychology 2(1), 141–165 (1981)
Kar, I., Parida, R.R., & Das, H.: Energy aware scheduling using genetic algorithm in cloud data centers. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 3545–3550. IEEE (2016)
Sarkhel, P., Das, H., Vashishtha, L.K.: Task-scheduling algorithms in cloud environment. In: Computational Intelligence in Data Mining, pp. 553–562. Springer, Singapore (2017)
Villena-Roman, J., Garcia-Morera, J., Moreno-Garcia, C. Ferrer-Urefia, L., Lana-Serrano, S., Carlos Gonzalez-Cristobal, J. Waterski. A. Martinez-Camara, E., umbreras, M.A. Martin-Valdivia, N1. T., Alfonso Urefia-Lopez, L.: TASS-Workshop on Sentiment Analysis at SEPLN, Workshop on Sentiment Analysis, Sociedad Espanola para el Procesamiento del Lenguaje (2012)
de Juan-Espinosa, M.: Personalidad Artificial: Hacia Una Simulación De Las Diferencias DePersonalidadEnSituaciones De Interacción. Universidad Autónoma de Madrid, Madrid (1997)
Polzehl, T., Möller, S., Metze, F.: Automatically assessing acoustic manifestations of personality in speech. In: Spoken Language Technology Workshop (SLT), pp. 7–12. IEEE (2010)
Francis, J.W.P.M.E., Booth, R.J.: Linguistic Inquiry and Word Count. Technical Report. Technical Report, Southern Methodist University, Dallas, TX (1993)
Ivanov, A.V., Riccardi, G., Sporka, A.J., Franc, J.: Recognition of personality traits from human spoken conversations. In: Twelfth Annual Conference of the International Speech Communication Association (2011)
Gill, A.J., & Oberlander, J.: Taking care of the linguistic features of extraversion. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 24, no. 24 (2002)
Acknowledgements
The authors are obliged for the guidance of ABES Engineering College staffs and faculties. Python mentor, Asst. Prof., Mr. Shubham Sidana, Asso. Prof., Mr. Abhishek Goyal, and evaluation team of experimental presentation to understand the concept well and for showing the path ahead. The acknowledgement to all those forces, which inspire us to work hard and to learn something and to make a difference in society.
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Rastogi, R. et al. (2020). Intelligent Analysis for Personality Detection on Various Indicators by Clinical Reliable Psychological TTH and Stress Surveys. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_12
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