A Bayesian framework for measuring association and its application to emotional dynamics in Web discourse
Abstract
This paper introduces a Bayesian framework designed to measure the degree of association between categorical random variables. The method is grounded in the formal definition of variable independence and is implemented using Markov Chain Monte Carlo (MCMC) techniques. Unlike commonly employed techniques in Association Rule Learning, this approach enables a clear and precise estimation of confidence intervals and the statistical significance of the measured degree of association. We applied the method to non-exclusive emotions identified by annotators in 4,613 tweets written in Portuguese. This analysis revealed pairs of emotions that exhibit associations and mutually opposed pairs. Moreover, the method identifies hierarchical relations between categories, a feature observed in our data, and is utilized to cluster emotions into basic-level groups.
Cite
@article{arxiv.2311.05330,
title = {A Bayesian framework for measuring association and its application to emotional dynamics in Web discourse},
author = {Henrique S. Xavier and Diogo Cortiz and Mateus Silvestrin and Ana Luísa Freitas and Letícia Yumi Nakao Morello and Fernanda Naomi Pantaleão and Gabriel Gaudencio do Rêgo},
journal= {arXiv preprint arXiv:2311.05330},
year = {2024}
}
Comments
9 pages, 2 tables, 4 figures. Accepted for publication at the Beyond Facts workshop of the Web Conference 2024