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Relevant bibliographies by topics / Personality prediction

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Academic literature on the topic 'Personality prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Personality prediction"

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S V, Vijay, Pradeep S, and Sathish N. "Personality Prediction System." International Journal of Research Publication and Reviews 4, no. 10 (October 2, 2023): 1707–17. http://dx.doi.org/10.55248/gengpi.4.1023.102653.

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Hall, Andrew N., and Sandra C. Matz. "Targeting Item–level Nuances Leads to Small but Robust Improvements in Personality Prediction from Digital Footprints." European Journal of Personality 34, no. 5 (September 2020): 873–84. http://dx.doi.org/10.1002/per.2253.

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In the past decade, researchers have demonstrated that personality can be accurately predicted from digital footprint data, including Facebook likes, tweets, blog posts, pictures, and transaction records. Such computer–based predictions from digital footprints can complement—and in some circumstances even replace—traditional self–report measures, which suffer from well–known response biases and are difficult to scale. However, these previous studies have focused on the prediction of aggregate trait scores (i.e. a person's extroversion score), which may obscure prediction–relevant information at theoretical levels of the personality hierarchy beneath the Big 5 traits. Specifically, new research has demonstrated that personality may be better represented by so–called personality nuances—item–level representations of personality—and that utilizing these nuances can improve predictive performance. The present work examines the hypothesis that personality predictions from digital footprint data can be improved by first predicting personality nuances and subsequently aggregating to scores, rather than predicting trait scores outright. To examine this hypothesis, we employed least absolute shrinkage and selection operator regression and random forest models to predict both items and traits using out–of–sample cross–validation. In nine out of 10 cases across the two modelling approaches, nuance–based models improved the prediction of personality over the trait–based approaches to a small, but meaningful degree (4.25% or 1.69% on average, depending on method). Implications for personality prediction and personality nuances are discussed. © 2020 European Association of Personality Psychology

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Li, Ze. "Prediction of MBTI Personality Leveraging Machine Learning Algorithms." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 580–87. http://dx.doi.org/10.54254/2755-2721/8/20230275.

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In this study, the author attempted to implement a machine learning approach to determine users' corresponding MBTI personality types by relying only on the content of their online forum postings. Models based on different algorithms are built and trained, and the natural language of the collected data set is converted into machine language for machine learning and used in subsequent tests to determine the correctness of the predicting results. The data set is collected from the forum and divided into two parts, the training set is leveraged to train the model and the test data set is leveraged to make personality predictions and compare with the training data set to measure the correctness of the predicting outcomes. The results show that logistic regression algorithm and vectorized representation of text with TfidfVectorizer can best accomplish the prediction task. This study completed a preliminary comparison of algorithms for personality prediction from text, which became the basis for subsequent personality model predictions using other media.

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Sireesha, Pendyala Sai. "Personality Prediction." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2876–81. http://dx.doi.org/10.22214/ijraset.2020.5482.

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Shenavi,, Sakshi. "PERSONALITY PREDICTION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 12, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33864.

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Personality prediction is a challenging yet crucial task in various fields such as psychology, human resources, and marketing. In this study, we propose a questionnaire-based approach using the random forest algorithm to predict personality traits. The questionnaire is designed to gather information related to the personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The dataset used for this study consists of responses from individuals who completed the questionnaire. Responses to specific questions are used as input variables for the random forest algorithm. The algorithm is trained on a portion of the dataset and then tested on the remaining portion to evaluate its performance in predicting personality traits. Our results show that the random forest algorithm achieves high accuracy in predicting personality traits, outperforming other machine learning algorithms such as logistic regression and support vector machines. This approach has the potential to be used in various applications, such as personalized marketing, recommendation systems, and mental health assessment. Key Words: Personality prediction, Random forest algorithm, personality traits, Questionnaire-based approach.

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Sunitha, Dr C., and Abirami N. "Personality Prediction Ocean Model Using Machine Learning." International Journal of Research Publication and Reviews 4, no. 10 (October 2, 2023): 1225–31. http://dx.doi.org/10.55248/gengpi.4.1023.102623.

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Artishcheva, Lira V., and Evgeniya A. Kuznetcova. "PERSONAL FEATURES OF ORPHANS IN PREDICTING." Volga Region Pedagogical Search 35, no. 1 (2021): 48–59. http://dx.doi.org/10.33065/2307-1052-2021-1-35-48-59.

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The relevance of the study is due to the existing problem of prediction. Orphans are in special life and social conditions, which determine their personal development and the formation of personal qualities. The research is aimed at revealing the relationship between the personality traits of orphans and probabilistic prediction. The aim of the study is to substantiate significant relationships between the signs of predictive abilities with such personal characteristics as resilience and self-esteem based on the analysis of the Pearson correlation statistical method. The research is aimed at solving the following issues: analysis of scientific works devoted to the problem of orphanhood; definition of the essence of the concepts of prediction, resilience, selfesteem; identification of the relationship between the signs of predictive ability and personality traits. According to the theory of probabilistic prediction, predicting the outcome of situations, the correctness of decision-making, as well as tactics of behavior depend on individual personality characteristics. As a result of the study, positive and negative significant interrelationships of indicators of predictive ability, resilience, and self-esteem were revealed. The results can be used in the field of psychology to improve the predictive ability of orphans.

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Saucier, Gerard, Kathryn Iurino, and Amber Gayle Thalmayer. "Comparing predictive validity in a community sample: High–dimensionality and traditional domain–and–facet structures of personality variation." European Journal of Personality 34, no. 6 (December 2020): 1120–37. http://dx.doi.org/10.1002/per.2235.

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Prediction of outcomes is an important way of distinguishing, among personality models, the best from the rest. Prominent previous models have tended to emphasize multiple internally consistent “facet” scales subordinate to a few broad domains. But such an organization of measurement may not be optimal for prediction. Here, we compare the predictive capacity and efficiency of assessments across two types of personality–structure model: conventional structures of facets as found in multiple platforms, and new high–dimensionality structures emphasizing those based on natural–language adjectives, in particular lexicon–based structures of 20, 23, and 28 dimensions. Predictions targeted 12 criterion variables related to health and psychopathology, in a sizeable American community sample. Results tended to favor personality–assessment platforms with (at least) a dozen or two well–selected variables having minimal intercorrelations, without sculpting of these to make them function as indicators of a few broad domains. Unsurprisingly, shorter scales, especially when derived from factor analyses of the personality lexicon, were shown to take a more efficient route to given levels of predictive capacity. Popular 20th–century personality–assessment models set out influential but suboptimal templates, including one that first identifies domains and then facets, which compromise the efficiency of measurement models, at least from a comparative–prediction standpoint. © 2020 European Association of Personality Psychology

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Muralidharan, Rohith, Neenu Kuriakose, and Sangeetha J. "Myers-Briggs Personality Prediction." Indian Journal of Data Mining 3, no. 1 (December 30, 2023): 11–19. http://dx.doi.org/10.54105/ijdm.b1630.053123.

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The Myers-Briggs Type Indicator (MBTI) is one of the most commonly used tool for assessing an individual's personality. This tool allows us to identify the psychological proclivity in the way they take decisions and perceive the world. MBTI has it’s applications spread across several fields which include career development and personal growth. This test consists of a set of questions which are specifically designed to evaluate and measure an individual's choices based on four dichotomies - Extraversion (E) vs. Introversion (I), Sensing (S) vs. Intuition (N), Thinking (T) vs. Feeling (F), and Judging (J) vs. Perceiving (P). Myers-Briggs Personality Prediction project aims to develop and deploy a system using machine learning which is capable of predicting one's MBTI personality type based on their online written interactions such as social media posts, comments, blogs etc. This project has significant implications for various applications, including improving customer experience, optimizing team dynamics, and developing personalized coaching programs. Through this project, we hope to gain a deeper understanding of how language use and personality type are related and to develop a robust tool for personality prediction.

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Eysenck, H. J. "Personality and prediction: Principles of personality assessment." Personality and Individual Differences 11, no. 1 (January 1990): 97. http://dx.doi.org/10.1016/0191-8869(90)90177-s.

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Dissertations / Theses on the topic "Personality prediction"

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Roberts, Amy B. "Physical activity prediction using transtheoretical model and personality /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1095437441&sid=8&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Weld, Suzanne Edna. "Stress management outcome: Prediction of differential outcome by personality characteristics." Thesis, University of Ottawa (Canada), 1992. http://hdl.handle.net/10393/7740.

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This study examined differential outcome between two stress management approaches according to subject characteristics. It was hypothesized that individuals with certain characteristics might benefit more from one form of stress management training (Rational Emotive Therapy) than another (Gendlin Focusing). The implication, were this hypothesis to be supported, would be that individuals could be streamed into one form of training versus another, according to certain personality variables, and the result would be greater effectiveness and efficiency in the delivery of services. Subjects were classed as having one of two sets of characteristics. One class of subjects (N = 34) displayed stress cognitively on the Cognitive-Somatic Anxiety Questionnaire (CSAQ) and were of the Sensing type on the Myers-Briggs Type Indicator (MBTI) while the other class of subjects (N = 31) displayed their stress somatically on the CSAQ and were of the Intuitive type on the MBTI. Based on personality theory, cognitively anxious Sensing types were predicted to have greater decreases in stress as a result of receiving a Rational Emotive Therapy approach to stress management while somatically anxious Intuitive types were predicted to have greater decreases in stress as a result of receiving a Gendlin Focusing approach to stress management. Additional measures of client characteristics were taken in order to explore the predictive potential of variables which the literature indicates might be useful in predicting differential outcome. These included Locus of Control, Verbal Reasoning, Abstract Reasoning, Experiencing Ability, Rational Beliefs. A measure of stress (Symptom Check List-90-R) was taken at three points in time: pre, post-training (or post-waiting as in the case of the waiting-list controls), and at one-month follow-up. A waiting-list control group (N = 30) was utilized to demonstrate a treatment versus no treatment comparison. Treatment group subjects received 12-15 hours of stress management training in either (Rational Emotive Therapy) RET or Focusing spread over 5 weeks. Half of each class of subjects received RET while the other half of each class of subjects received Focusing. Key results include: Both classes of treatment subjects displayed significantly greater reductions (p .05) in stress levels after training as compared to waiting-list control subjects who had not received training over the same period of time. The main hypothesis was not supported. There were no statistically significant treatment by classification interactions. However, there were relationships between client satisfaction, use of the techniques, and decrease in reported levels of stress. Further analyses showed that none of the variables were significant predictors of outcome. This Study introduced Focusing as a viable stress management technique.

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Lloyd, Adele Sian. "Use of personality assessment for the prediction of behaviour in horses." Thesis, Open University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491596.

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There are several potential applications for horse personality assessment, but first a reliable and valid form of assessment must be developed. The primary aim of this thesis was to develop a horse personality assessment method and test it for both reliability and validity by relating personality assessments to real-world observations. A 3D-item rating questionnaire was developed and was named the Horse Personality Questionnaire (HPQ). This was used to assess 61 horses, each by three raters. 71.2% of horses and 25 of the items were rated consistently between raters. Principal component analysis (peA) on these data extracted six components that were thought to describe horse personality; Antagonism, Anxiousness, Activity, Protection, Sociability and Inquisitiveness. Personality component scores were found to correlate with horse behaviours recorded in the field, thus demonstrating the reliability and validity of the HPQ. The HPQ was also used to explore breed differences in horse personality, with 1223 horses from eight different breeds assessed. The results provided strong evide.,nce that horse breeds differ in personality, but those breeds with linked pedigrees or functions were shown to be more alike. These results suggested that personality in horses could be, to some extent, heritable and that humans have selected for different personality types. During the third study predictions of personality behaviour correlations were tested by first assessing 14 horses and then exposing each horse to three behaviour tests (learning, arena and turn-out tests). Of 25 predicted behaviours none were found to be significant. These results did not support previous indications that personality scores could be used to predict behaviour. It was concluded that the behaviour tests used and the predictions made may not have been suitable. The three experiments are discussed in terms of their implications for personality research and the potential applications of the HPQ in the equine industry. It was concluded that the HPQ was a reliable assessment method but required further development and testing prior to application in the equine industry.

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Cowles, Natalie Shapiro Steven K. "Prediction of adolescent suicidality relative contribution of diagnosis, psychotherapy, and impulsity /." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Fall/Dissertations/COWLES_NATALIE_56.pdf.

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Taylor, Pamela J. "The biosocial model of personality : application to the prediction of alcohol consumption /." St. Lucia, Qld, 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17960.pdf.

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Wiita, Nathan Ellis. "Voluntary turnover prediction comparing the utility of implicit and explicit personality measures /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31786.

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Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2010.
Committee Chair: Lawrence R. James; Committee Member: Jack Feldman; Committee Member: Richard Catrambone. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Shoots-Reinhard, Brittany. "Using personality confidence to enhance behavioral prediction the case of self-monitoring /." Connect to resource, 2006. http://hdl.handle.net/1811/6485.

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Thesis (Honors)--Ohio State University, 2006.
Title from first page of PDF file. Document formatted into pages: contains 21 p.; also includes graphics. Includes bibliographical references (p. 16-18). Available online via Ohio State University's Knowledge Bank.

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Kabat, Robert. "Dark Triad Personality Traits Prediction of Managers' Mind-set in Business Organizations." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6841.

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Presence of the Dark Triad traits of Machiavellianism, narcissism, and psychopathy in the workplace, especially among managers, has received increased attention due to the implications for organizations. One way to influence behavior in business organizations is through controlled interventions to change individual mind-sets. A review of the extant literature indicated that the relationship between Dark Triad traits and mind-set had not been sufficiently examined. A quantitative study was conducted to examine whether a manager's Dark Triad traits predict their mindset. To explore this relationship, a sample of 153 managers' responses was collected online. The Short Dark Triad measure was used to assess participants' Machiavellianism, narcissism, and psychopathy and the Implicit Theory Measure was used to assess mind-set. Regression showed that only Machiavellianism predicts mind-set, accounting for 7% of the variance in mind-set as the criterion variable. The findings indicate that the relationship between Dark Triad traits and mind-set is weaker than a review of the extant psychological literature might suggest. Additionally, this study found that the Dark Triad traits are significantly negatively correlated with manager age, which might provide a new direction for further research. Further research on how and why Dark Triad traits tend to decline with age is recommended. The present study suggests that a better understanding of the relationship between the Dark Triad traits and mind-set and the knowledge that controlled interventions aimed at promoting a growth mind-set are most likely not a useful tool to mitigate the level of Dark Triad psychological traits of managers in business organizations.

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Marshall, Margaret A. "The traits as situational sensitivities (TASS) model : a more accurate way to predict behavior /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/8991.

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O'Connor, Peter J. "The mediation of temperament by character in the prediction of workplace outcomes." Thesis, The University of Queensland, 2007. https://eprints.qut.edu.au/102482/1/Peter_O%27Connor_thesis.pdf.

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Dimensional models of personality have typically sought to explore personality structure principally from a descriptive perspective, and as such make for effective personality taxonomies. Fortunately, personality research is currently in transition, with researchers looking towards more complex, scientifically derived theories of personality in an attempt to learn about the biological and cognitive mechanisms underlying surface level personality dimensions. The purpose of the current research program was to test numerous structural models of the relationship between two bio-cognitive models of personality and indices of workplace behaviour. It was argued that Cloninger, Svrakic and Przybeck’s (1993) scales of personality can be modelled according to an approach/avoidance framework, and that character dimensions mediate temperament in the prediction of important workplace behaviours. It was also argued that Jackson’s (2005) scales of personality can be modelled according to an approach framework, and that Goal Orientation mediates Sensation Seeking in the prediction of important workplace behaviours. Chapter 1 begins with a brief introduction to the study of personality from the trait perspective. This is followed by a brief overview of well known biological models of personality (e.g., Eysenck, 1967; Gray, 1982, 1987), leading to a more in depth discussion of both Cloninger et al.’s (1993) and Jackson’s (2005) models of personality. Cloninger et al.’s (1993) model is comprehensively reviewed and critiqued, based on clinical, genetic, psychometric and neurological research. A complex, structural model of Cloninger’s et al.’s (1993) scales of personality is then proposed. It is argued that Cloninger et al.’s dimensions can be modelled along an approach and avoidance theme, and that character mediates temperament in the prediction of important workplace behaviours. The introduction concludes with an overview of Jackson’s (2005) model, and key similarities between Jackson’s model and the proposed structural model of Cloninger et al.’s dimensions are highlighted. The introduction focuses on justifying the proposed mediation between temperament and character in the prediction of workplace variables. Chapter’s 2, 3 and 4 provide empirical tests of the key hypotheses outlined in the introduction. In chapter 2, an initial, basic test of Cloninger et al.’s model was conducted, whereby the model was compared to the Big Five (Costa & McCrae, 1985) in its ability to predict leadership emergence. It was found that a substantial portion of the variance in leadership emergence was trait based, and that that the multilevel model incorporating Cloninger et al.’s (1993) dimensions provided the best fit. The purpose of chapter 3 was to assess the proposed structural model of Cloninger et al.’s (1993) personality dimensions, and to assess the utility of the model in the prediction of workplace outcomes. The results of the two studies presented in this chapter were generally consistent with the proposed structure of Cloninger et al.’s dimensions. Cloninger et al.’s model was also found to significantly predict several workplace outcomes. In chapter 4, an alternative model of temperament and character was explored. Jackson’s (2005) model suggests that Goal Orientation mediates Sensation Seeking in the prediction of functional behaviours (i.e. an approach pathway). In this chapter, two central components of the model were tested across two studies. Regression analyses in both studies generally supported the proposed model and were consistent with the theme that character mediates temperament in the prediction of workplace variables. A number of conclusions are made from this research. Firstly, it is argued that biological models of personality, particularly Cloninger et al.’s and Jackson’s have utility in the area of Organisational Psychology. It is argued that models of personality which recognise the differential influence of temperament and character are likely to lead to a number of accurate and interesting implications. Specifically, it is suggested that dimensions of character are more open to training and intervention than are temperament dimensions.

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Books on the topic "Personality prediction"

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Wiggins, Jerry S. Personality and prediction: Principles of personality assessment. Malabar, Fla: R.E. Krieger Pub. Co., 1988.

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Criminal psychology and personality profiling. Philadelphia: Mason Crest Publishers, 2006.

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Melʹnikov, V. M. Psikhologicheskiĭ portret lichnosti. Ulan-Udė: Buri︠a︡tskiĭ gos. universitet, 2008.

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Melʹnikov, V. M. Psikhologicheskiĭ portret lichnosti. Ulan-Udė: Buri︠a︡tskiĭ gos. universitet, 2008.

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Find out who's normal and who's not: The proven system to quickly assess anyone's emotional stability. Lakewood, NJ: Viter Press, 2009.

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Adrian, Furnham. Personality at Work. London: Taylor & Francis Inc, 2002.

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Adrian, Furnham. Personality at Work. London: Taylor & Francis Group Plc, 2004.

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Harmening, William M. The criminal triad: Psychosocial development of the criminal personality type. Springfield, Ill: Charles C Thomas, 2010.

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Harmening, William M. The criminal triad: Psychosocial development of the criminal personality type. Springfield, Ill: Charles C Thomas, 2010.

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The criminal triad: Psychosocial development of the criminal personality type. Springfield, Ill: Charles C Thomas, 2010.

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Book chapters on the topic "Personality prediction"

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Solinger, Carrie, Leanne Hirshfield, Stuart Hirshfield, Rachel Friendman, and Christopher Leper. "Beyond Facebook Personality Prediction:." In Social Computing and Social Media, 486–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07632-4_46.

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Mishra, Sourav, and Santwana Sagnika. "A Review on Personality Prediction." In Advances in Power Systems and Energy Management, 61–70. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_7.

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Grossarth-Maticek, R., and H. J. Eysenck. "Personality and Cancer: Prediction and Prophylaxis." In Anticarcinogenesis and Radiation Protection 2, 451–58. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3850-9_63.

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Arora, Alakh, and N. K. Arora. "Personality Prediction System Through CV Analysis." In Advances in Intelligent Systems and Computing, 337–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1518-7_28.

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Kishore Kumar, R., V. Jeeva Surya, and J. Shana. "Personality Prediction Based on Twitter Tweets." In Computer Vision and Robotics, 25–34. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8225-4_3.

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Despotovski, Filip, and Sonja Gievska. "An In-Depth Analysis of Personality Prediction." In Communications in Computer and Information Science, 134–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33110-8_12.

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Agarwal, Navanshu, Lokesh Chouhan, Ishita Parmar, Sheirsh Saxena, Ridam Arora, Shikhin Gupta, and Himanshu Dhiman. "Personality Prediction and Classification Using Twitter Data." In Social Networking and Computational Intelligence, 707–16. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2071-6_59.

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Bhamare, Mamta, and K. Ashokkumar. "Prediction of Personality Traits in Facebook Users." In Advances in Data Science and Management, 127–36. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5685-9_13.

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Minu, R. I., and G. Nagarajan. "Personality Traits Prediction in Facebook: A Review." In Advances in Power Systems and Energy Management, 451–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_43.

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Suman, Chanchal, Sriparna Saha, and Pushpak Bhattacharyya. "Emotion-Aided Multi-modal Personality Prediction System." In Communications in Computer and Information Science, 289–301. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1645-0_24.

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Conference papers on the topic "Personality prediction"

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Aran, Oya, and Daniel Gatica-Perez. "Cross-domain personality prediction." In the 15th ACM. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2522848.2522858.

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Dutta, Indira, R. Athilakshmi, and Amulya. "Personality Prediction Using Deep Learning." In 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2023. http://dx.doi.org/10.1109/icaect57570.2023.10117573.

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Patil, Shankar M., Riya Singh, Paresh Patil, and Neha Pathare. "Personality prediction using Digital footprints." In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432380.

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Al Maruf, Abdullah, Md Abdullah-Al Nayem, Md Mahmudul Haque, Zakaria Masud Jiyad, Al Mamun Or Rashid, and Fahima Khanam. "A Survey on Personality Prediction." In ICCA 2022: 2nd International Conference on Computing Advancements. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3542954.3543012.

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Vijay, Hima, and Neenu Sebastian. "Personality Prediction using Machine Learning." In 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). IEEE, 2022. http://dx.doi.org/10.1109/ic3sis54991.2022.9885425.

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K, Pooja, Rakshitha GH, Spoorthi S, Shoaib Kamal, Trupthi Rao, and Ashwini K. "Personality Prediction Using Machine Learning." In 2023 International Conference on Computational Intelligence for Information, Security and Communication Applications (CIISCA). IEEE, 2023. http://dx.doi.org/10.1109/ciisca59740.2023.00059.

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Nivetha, S. K., M. Geetha, R. S. Latha, K. Sneha, S. Sobika, and C. Yamuna. "Personality Prediction for Online Interview." In 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2022. http://dx.doi.org/10.1109/iccci54379.2022.9740980.

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Feng, Yuanyuan, and Kejian Liu. "A Personality Prediction Method of WEIBO Users based on Personality Lexicon." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112312.

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Personality is the dominant factor affecting human behavior. With the rise of social network platforms, increasing attention has been paid to predict personality traits by analyzing users' behavior information, and pay little attention to the text contents, making it insufficient to explain personality from the perspective of texts. Therefore, in this paper, we propose a personality prediction method based on personality lexicon. Firstly, we extract keywords from texts, and use word embedding techniques to construct a Chinese personality lexicon. Based on the lexicon, we analyze the correlation between personality traits and different semantic categories of words, and extract the semantic features of the texts posted by Weibo users to construct personality prediction models using classification algorithm. The final experiments shows that compared with SC-LIWC, the personality lexicon constructed in this paper can achieve a better performance.

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Parameswara, Made Arbi, Farrel Satya Putra Mahendra, Muhammad Randy Cahya Mardika, Ira Puspasari, and Nugraha Priya Utama. "Big Five Personality Prediction Based on Indonesian Tweets and Personality Test." In 2023 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2023. http://dx.doi.org/10.1109/iceei59426.2023.10346812.

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Jaffer, Anum, Sara Ali, Fahad Iqbal Khawaja, Yasar Ayaz, Muhammad Sajid, and Umer Asgher. "Personality Prediction in Human-Robot-Interaction (HRI)." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001601.

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For an efficient and smooth human-robot interaction, communication protocols such as verbal and non-verbal communication, emotions, and personality plays an important role. Human-Robot-Interaction is an emerging field and robots are now a part of daily life where it can grasp both verbal and non-verbal cues. Personality prediction is an important research area in Human-Robot Interaction (HRI). Several important question in personality prediction includes: which personality traits will be important and which specific phycology model and robot do researchers use? Secondly, how emotions, facial expression, paralanguage, and bodily movements are related to personality traits? And finally, how will we acquire data to train a robot and what kind of questionnaires can be used? With the support of prior research studies and experiments, this paper will contribute towards developing the ground basis for personality prediction using a robot.

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Reports on the topic "Personality prediction"

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Nagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40700.

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The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.

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Kilbourne, Brock, Susan M. Hilton, and Jerry Goodman. Predicting Personality Disorder Diagnosis of Hospitalized Navy Personnel. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada206058.

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Nagahi, Morteza, Raed Jaradat, Safae El Amrani, Michael Hamilton, and Simon Goerger. Holistic and reductionist thinker : a comparison study based on individuals’ skillset and personality types. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40746.

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As organizations operate in turbulent and complex environments, it has become a necessity to assess the systems thinking (ST) skills, personality types (PTs), and demographics of practitioners. In this study, we investigated the relationship between practitioners’ ST profile, their PTs profiles and demographic characteristics in the domain of complex system problems. The objective of this study is to address the current gap in the literature – lack of studies dedicated to predicting practitioners’ ST profile based on their PTs and demographics characteristics. A total of 258 practitioners with different demographics and PTs provided the data. The results show that (1) practitioners can be classified based on their ST skills scores into two clusters: holistic and reductionist (that is, ST profile), (2) each cluster has different PTs profiles and demographic characteristics, and (3) practitioner’s ST profile can be predicted, with good accuracy, based on their PTs profile and demographic characteristics.

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Putka, Dan J., Matthew Fleisher, and Adam Beatty. Enhancing the Predictive Potential of Personality: Isolating Multiple Components of Trait Expression via a Single Administration Design. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada618190.

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Berlin, Noémie, Jan Dul, Marco Gazel, Louis Lévy-Garboua, and Todd Lubart. Creative Cognition as a Bandit Problem. CIRANO, May 2023. http://dx.doi.org/10.54932/anre7929.

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This paper characterizes creative cognition as a multi-armed bandit problem involving a trade-off between exploration and exploitation in sequential decisions from experience taking place in novel uncertain environments. Creative cognition implements an efficient learning process in this kind of dynamic decision. Special emphasis is put on the optimal sequencing of divergent and convergent behavior by showing that divergence must be inhibited at one point to converge toward creative behavior so that excessive divergence is counterproductive. We test this hypothesis in two behavioral experiments, using both novel and well-known tasks and precise measures of individual differences in creative potential in middle and high school students. Results in both studies confirmed that a task-dependent mix of divergence and convergence predicted high performance in a production task and better satisfaction in a consumption task, but exclusively in novel uncertain environments. These predictions were maintained after controlling for gender, personality, incentives, and other factors. As hypothesized, creative cognition was shown to be necessary for high performance under the appropriate conditions. However, it was not necessary for getting high grades in a traditional school system.

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Bakaç, Cafer, Jetmir Zyberaj, and James C. Barela. Predicting telecommuting preferences and job outcomes amid COVID-19 pandemic : A latent profile analysis. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49214.

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Telecommuting is defined as “a work practice that involves members of an organization substituting a portion of their typical work hours (ranging from a few hours per week to nearly full-time) to work away from a central workplace—typically principally from home —using technology to interact with others as needed to conduct work tasks”(Allen, Golden, & Shockley, 2015: 44). This kind of practice substantially differs from the regular and ordinary modes of work because employees perform their usual work in different settings, usually from home (Allen et al., 2015). Although research has been conducted on telecommuting since the 1970s, it has recently become critical when life incidents, like the COVID-19 pandemic has forced many to work from home. Such events offer rare opportunities, for a wide range of researchers and from various fields, to study important questions that would not typically be able to be asked, such as about telecommuting experiences. We took this opportunity and conducted two studies regarding telecommuting, basing our rationale on the fact that many on-site employees were forced to work from home, across a wide range of occupations as a direct result of the pandemic(Kramer & Kramer, 2020). The aim of our study, thus, was to investigate the preferences of employees who were forced to work from home. Specifically, bycreating latent profiles from important work and personality related constructs, we aimed at predicting employees’ preference for working from home or working on-site based on these profiles, and further investigate the relationship of these latent profiles to perceived productivity, job satisfaction, and job engagement.

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