Tecla: A temperament and psychological type prediction framework from Twitter data
| dc.contributor.author | Lima A.C.E.S. | |
| dc.contributor.author | De Castro L.N. | |
| dc.date.accessioned | 2024-03-12T23:53:45Z | |
| dc.date.available | 2024-03-12T23:53:45Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | © 2019 Lima, de Castro. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user’s social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey’s model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair. | |
| dc.description.issuenumber | 3 | |
| dc.description.volume | 14 | |
| dc.identifier.doi | 10.1371/journal.pone.0212844 | |
| dc.identifier.issn | 1932-6203 | |
| dc.identifier.uri | https://dspace.mackenzie.br/handle/10899/35300 | |
| dc.relation.ispartof | PLoS ONE | |
| dc.rights | Acesso Aberto | |
| dc.title | Tecla: A temperament and psychological type prediction framework from Twitter data | |
| dc.type | Artigo | |
| local.scopus.citations | 9 | |
| local.scopus.eid | 2-s2.0-85062869358 | |
| local.scopus.subject | Behavioral Research | |
| local.scopus.subject | Female | |
| local.scopus.subject | History, 21st Century | |
| local.scopus.subject | Humans | |
| local.scopus.subject | Machine Learning | |
| local.scopus.subject | Male | |
| local.scopus.subject | Models, Psychological | |
| local.scopus.subject | Psycholinguistics | |
| local.scopus.subject | Social Behavior | |
| local.scopus.subject | Social Media | |
| local.scopus.subject | Temperament | |
| local.scopus.updated | 2024-05-01 | |
| local.scopus.url | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85062869358&origin=inward |