Predicting temperament using keirsey’s model for Portuguese twitter data

dc.contributor.authorClaro C.F.
dc.contributor.authorLima A.C.E.S.
dc.contributor.authorDe Castro L.N.
dc.description.abstractCopyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.Temperament is a set of innate tendencies of the mind related with the processes of perceiving, analyzing and decision making. The purpose of this paper is to predict the user's 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 Forest algorithms 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. By using binary classifiers the average accuracy was 90.93% for the artisan temperament, 88.98% for the guardian, 51.98% for the idealist and 71.42% for the Rational.
dc.relation.ispartofICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence
dc.rightsAcesso Aberto
dc.subject.otherlanguageKeirsey Temperament Model
dc.subject.otherlanguageMachine Learning
dc.subject.otherlanguageSocial Media
dc.subject.otherlanguageTemperament’s Classification
dc.titlePredicting temperament using keirsey’s model for Portuguese twitter data
dc.typeArtigo de evento
local.scopus.subjectBinary classifiers
local.scopus.subjectRandom forest algorithm
local.scopus.subjectS models
local.scopus.subjectSocial media