Cluster Fusion Training: Exploring Cluster Analysis to Enhance Cross-Domain Sentiment Classification

  • Victor Akihito Kamada Tomita Universidade de São Paulo
  • Angelo Cesar Mendes da Silva Universidade de São Paulo
  • Ricardo Marcondes Marcacini Universidade de São Paulo


Devido à escassez de dados para domínios específicos, muitos estudos optam por treinar modelos em domínios cruzados. A abordagem mais comum consiste em treinar modelos em todos os domínios-fonte e, em seguida, validar seu desempenho no domínio-alvo. Mas, essa abordagem não leva em consideração que diferentes palavras podem ter semânticas distintas dependendo do domínio. Neste artigo, é proposto um novo método que usa técnicas de clustering para agrupar dados semelhantes. A partir desses grupos, são trainados modelos especialistas que são usados em um processo de fusão. Através desse método, são demonstradas melhorias até significativas de até 5% de acurácia para modelos de classificação.

Palavras-chave: análise de sentimentos, domínio cruzado, agrupamento


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TOMITA, Victor Akihito Kamada; DA SILVA, Angelo Cesar Mendes; MARCACINI, Ricardo Marcondes. Cluster Fusion Training: Exploring Cluster Analysis to Enhance Cross-Domain Sentiment Classification. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 330-344. ISSN 2763-9061. DOI: