Predicting Temperament using Keirsey’s Model for Portuguese Twitter Data

Cristina Fátima Claro, Ana Carolina E. S. Lima, Leandro N. de Castro

Abstract

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 train-ing 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.

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Paper Citation


in Harvard Style

Claro C., Lima A. and de Castro L. (2018). Predicting Temperament using Keirsey’s Model for Portuguese Twitter Data.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 250-256. DOI: 10.5220/0006700102500256


in Bibtex Style

@conference{icaart18,
author={Cristina Fátima Claro and Ana Carolina E. S. Lima and Leandro N. de Castro},
title={Predicting Temperament using Keirsey’s Model for Portuguese Twitter Data},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={250-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006700102500256},
isbn={978-989-758-275-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Predicting Temperament using Keirsey’s Model for Portuguese Twitter Data
SN - 978-989-758-275-2
AU - Claro C.
AU - Lima A.
AU - de Castro L.
PY - 2018
SP - 250
EP - 256
DO - 10.5220/0006700102500256