A Bayesian Framework for Measuring Association and Its Application to Emotional Dynamics in Web Discourse

Página do item simplificado
dc.contributor.authorXavier H.S.
dc.contributor.authorCortiz D.
dc.contributor.authorSilvestrin M.
dc.contributor.authorFreitas A.L.
dc.contributor.authorMorello L.Y.N.
dc.contributor.authorPantaleao F.N.
dc.contributor.authordo Rego G.G.
dc.date.accessioned2024-07-01T06:10:46Z
dc.date.available2024-07-01T06:10:46Z
dc.date.issued2024
dc.description.abstract© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.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.
dc.description.firstpage1450
dc.description.lastpage1458
dc.identifier.doi10.1145/3589335.3651911
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/38803
dc.relation.ispartofWWW 2024 Companion - Companion Proceedings of the ACM Web Conference
dc.rightsAcesso Aberto
dc.subject.otherlanguageassociation
dc.subject.otherlanguagecategorical variables
dc.subject.otherlanguageemotions
dc.subject.otherlanguagesentiment analysis
dc.titleA Bayesian Framework for Measuring Association and Its Application to Emotional Dynamics in Web Discourse
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85194477056
local.scopus.subjectBayesian frameworks
local.scopus.subjectCategorical variables
local.scopus.subjectConfidence interval
local.scopus.subjectDegree of association
local.scopus.subjectEmotion
local.scopus.subjectFormal definition
local.scopus.subjectITS applications
local.scopus.subjectMarkov chain Monte Carlo techniques
local.scopus.subjectSentiment analysis
local.scopus.subjectStatistical significance
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85194477056&origin=inward
Arquivos
Coleções
Artigos de periódico