  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.
