The application of Keirsey’s temperament model to twitter data in portuguese
| dc.contributor.author | Claro C.F. | |
| dc.contributor.author | Lima A.C.E.S. | |
| dc.contributor.author | de Castro L.N. | |
| dc.date.accessioned | 2024-03-12T23:55:46Z | |
| dc.date.available | 2024-03-12T23:55:46Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | © Springer Nature Switzerland AG 2019.Temperament is a set of innate tendencies of the mind related with the processes of perception, analysis and decision making. The purpose of this paper is to predict Twitter users temperament based on Portuguese tweets and following Keirsey’s model, which classifies the temperament into artisan, guardian, idealist and rational. The proposed methodology uses a Portuguese version of LIWC, which is a dictionary of words, to analyze the context of words, and supervised learning using the KNN, SVM and Random Forests for training the classifiers. The resultant average accuracy obtained was 88.37% for the artisan temperament, 86.92% for the guardian, 55.61% for the idealist, and 69.09% for the rational. For classification using TF-IDF the SVM algorithm obtained the best performance to the artisan temperament with average accuracy of 88.28%. | |
| dc.description.firstpage | 408 | |
| dc.description.lastpage | 421 | |
| dc.description.volume | 11352 LNAI | |
| dc.identifier.doi | 10.1007/978-3-030-05453-3_19 | |
| dc.identifier.issn | 1611-3349 | |
| dc.identifier.uri | https://dspace.mackenzie.br/handle/10899/35411 | |
| dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
| dc.rights | Acesso Restrito | |
| dc.subject.otherlanguage | Keirsey temperament model | |
| dc.subject.otherlanguage | Machine learning | |
| dc.subject.otherlanguage | Social media | |
| dc.title | The application of Keirsey’s temperament model to twitter data in portuguese | |
| dc.type | Artigo de evento | |
| local.scopus.citations | 1 | |
| local.scopus.eid | 2-s2.0-85059666677 | |
| local.scopus.subject | Random forests | |
| local.scopus.subject | S models | |
| local.scopus.subject | Social media | |
| local.scopus.subject | SVM algorithm | |
| local.scopus.subject | Twitter datum | |
| local.scopus.updated | 2024-05-01 | |
| local.scopus.url | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059666677&origin=inward |