{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:19:26Z","timestamp":1761581966211,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T00:00:00Z","timestamp":1590019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Processes"],"abstract":"<jats:p>Goldberg\u2019s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance.<\/jats:p>","DOI":"10.3390\/pr8050618","type":"journal-article","created":{"date-parts":[[2020,5,22]],"date-time":"2020-05-22T10:18:18Z","timestamp":1590142698000},"page":"618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1561-2897","authenticated-orcid":false,"given":"Bruno","family":"Fernandes","sequence":"first","affiliation":[{"name":"Department of Informatics, ALGORITMI Centre, University of Minho, 4704-553 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3444-4393","authenticated-orcid":false,"given":"Alfonso","family":"Gonz\u00e1lez-Briones","sequence":"additional","affiliation":[{"name":"Research Group on Agent-Based, Social and Interdisciplinary Applications (GRASIA), Complutense University of Madrid, 28040 Madrid, Spain"},{"name":"BISITE Research Group, University of Salamanca, Edificio I+D+i, 37007 Salamanca, Spain"}]},{"given":"Paulo","family":"Novais","sequence":"additional","affiliation":[{"name":"Department of Informatics, ALGORITMI Centre, University of Minho, 4704-553 Braga, Portugal"}]},{"given":"Miguel","family":"Calafate","sequence":"additional","affiliation":[{"name":"Department of Informatics, ALGORITMI Centre, University of Minho, 4704-553 Braga, Portugal"}]},{"given":"Cesar","family":"Analide","sequence":"additional","affiliation":[{"name":"Department of Informatics, ALGORITMI Centre, University of Minho, 4704-553 Braga, Portugal"}]},{"given":"Jos\u00e9","family":"Neves","sequence":"additional","affiliation":[{"name":"Department of Informatics, ALGORITMI Centre, University of Minho, 4704-553 Braga, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.paid.2012.01.029","article-title":"Direct and indirect relations between the Big 5 personality traits and aggressive and violent behavior","volume":"52","author":"Barlett","year":"2012","journal-title":"Personal. Individ. Differ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rothmann, S., and Coetzer, E.P. (2003). The big five personality dimensions and job performance. J. Ind. Psychol., 29.","DOI":"10.4102\/sajip.v29i1.88"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s12144-005-1028-3","article-title":"Big-five personality differences of cheaters and non-cheaters","volume":"24","author":"Orzeck","year":"2005","journal-title":"Curr. Psychol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kazdin, A. (2000). Encyclopedia of Psychology, American Psychological Association.","DOI":"10.1037\/10523-000"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1207\/s15327043hup1102&3_8","article-title":"Big Five Personality Dimensions and Job Performance in Army and Civil Occupations: A European Perspective","volume":"11","author":"Salgado","year":"1998","journal-title":"Hum. Perform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1080\/00223890902935878","article-title":"The HEXACO\u201360: A Short Measure of the Major Dimensions of Personality","volume":"91","author":"Ashton","year":"2009","journal-title":"J. Personal. Assess."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Myers, I. (1962). The Myers-Briggs Type Indicator: Manual (1962), Consulting Psychologists Press.","DOI":"10.1037\/14404-000"},{"key":"ref_8","unstructured":"Riso, D., and Hudson, R. (2000). Understanding the Enneagram: The Practical Guide to Personality Types, Houghton Mifflin Harcourt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1207\/s15327752jpa6401_2","article-title":"Domains and facets: Hierarchical personality assessment using the Revised NEO Personality Inventory","volume":"64","author":"Costa","year":"1995","journal-title":"J. Personal. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1037\/1040-3590.4.1.26","article-title":"The development of markers for the Big-Five factor structure","volume":"4","author":"Goldberg","year":"1992","journal-title":"Psychol. Assess."},{"key":"ref_11","first-page":"102","article-title":"The Big Five trait taxonomy: History, measurement, and theoretical perspectives","volume":"Volume 2","author":"John","year":"1999","journal-title":"Handbook of Personality: Theory and Research"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s10994-013-5415-y","article-title":"Manifestations of user personality in website choice and behaviour on online social networks","volume":"95","author":"Kosinski","year":"2014","journal-title":"Mach. Learn."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1207\/s15327752jpa6303_8","article-title":"Mini-Markers: A brief version of Goldberg\u2019s unipolar Big-Five markers","volume":"63","author":"Saucier","year":"1994","journal-title":"J. Personal. Assess."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MIS.2017.23","article-title":"Deep Learning-Based Document Modeling for Personality Detection from Text","volume":"32","author":"Majumder","year":"2017","journal-title":"IEEE Intell. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, J., and Markov, K. (2017, January 8). Deep learning based personality recognition from Facebook status updates. Proceedings of the IEEE 8th International Conference on Awareness Science and Technology (iCAST), Taichung, Taiwan.","DOI":"10.1109\/ICAwST.2017.8256484"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sumner, C., Byers, A., Boochever, R., and Park, G. (2012, January 12\u201315). Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets. Proceedings of the 11th International Conference on Machine Learning and Applications, Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2012.218"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1037\/0022-3514.77.6.1296","article-title":"Linguistic styles: Language use as an individual difference","volume":"77","author":"Pennebaker","year":"1999","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1037\/pspp0000020","article-title":"Automatic personality assessment through social media language","volume":"108","author":"Park","year":"2015","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Schwartz, H., Eichstaedt, J., Kern, M., Dziurzynski, L., Ramones, S., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., and Seligman, M. (2013). Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0073791"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jneumeth.2017.10.023","article-title":"Personality biomarkers of pathological gambling: A machine learning study","volume":"294","author":"Cerasa","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mehta, Y., Majumder, N., Gelbukh, A., and Cambria, E. (2019). Recent trends in deep learning based personality detection. Artif. Intell. Rev.","DOI":"10.1007\/s10462-019-09770-z"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Levitan, S., Levitan, Y., An, G., Levine, M., Levitan, R., Rosenberg, A., and Hirschberg, J. (2016, January 17). Identifying Individual Differences in Gender, Ethnicity, and Personality from Dialogue for Deception Detection. Proceedings of the Second Workshop on Computational Approaches to Deception Detection, San Diego, CA, USA.","DOI":"10.18653\/v1\/W16-0806"},{"key":"ref_23","unstructured":"Levitan, S., Levine, M., Hirschberg, J., Cestero, N., An, G., and Rosenberg, A. (2020, March 03). Individual Differences in Deception and Deception Detection. Available online: Https:\/\/Www.Semanticscholar.Org\/Paper\/Individual-Differences-In-Deception-And-Deception-Levitan-Levine\/295332ebfb77387f4ccbacbd214edf72caf3e331."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gurpinar, F., Kaya, H., and Salah, A. (2016, January 11\u201314). Combining Deep Facial and Ambient Features for First Impression Estimation. Proceedings of the Computer Vision\u2014ECCV 2016 Workshops, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-49409-8_30"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"G\u00fc\u00e7l\u00fct\u00fcrk, Y., G\u00fc\u00e7l\u00fc, U., P\u00e9rez, M., Escalante, H., Bar\u00f3, X., Andujar, C., Guyon, I., Junior, J., Madadi, M., and Escalera, S. (2017, January 22\u201329). Visualizing Apparent Personality Analysis with Deep Residual Networks. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.367"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, H., Wei, X., and Wu, J. (2016, January 11\u201314). Deep Bimodal Regression for Apparent Personality Analysis. Proceedings of the Computer Vision\u2014ECCV 2016 Workshops, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-49409-8_25"},{"key":"ref_27","unstructured":"Wessa, P. (2020, May 01). Cronbach alpha (v1.0.5) in Free Statistics Software (v1.2.1). Available online: https:\/\/www.wessa.net\/rwasp_cronbach.wasp\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_29","first-page":"2079","article-title":"On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","first-page":"331","article-title":"Magnetic resonance imaging study of gray matter in schizophrenia based on XGBoost","volume":"17","author":"Yu","year":"2018","journal-title":"J. Integr. Neurosci."},{"key":"ref_31","first-page":"11","article-title":"Single-Sentence Compression using XGBoost","volume":"9","author":"Sahoo","year":"2019","journal-title":"Int. J. Inf. Retr. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pesantez-Narvaez, J., Guillen, M., and Alca\u00f1iz, M. (2019). Predicting Motor Insurance Claims Using Telematics Data \u2014 XGBoost versus Logistic Regression. Risks, 7.","DOI":"10.20944\/preprints201905.0122.v1"}],"container-title":["Processes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9717\/8\/5\/618\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:31:21Z","timestamp":1760175081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9717\/8\/5\/618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,21]]},"references-count":32,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["pr8050618"],"URL":"https:\/\/doi.org\/10.3390\/pr8050618","relation":{},"ISSN":["2227-9717"],"issn-type":[{"type":"electronic","value":"2227-9717"}],"subject":[],"published":{"date-parts":[[2020,5,21]]}}}