Análise de Métodos de Extração de Aspectos em Opiniões Regulares

Autores

Palavras-chave:

Mineração de Opinião, Análise de Sentimentos, Extração de Aspectos

Resumo

Um sistema de Mineração de Opinião consiste de identificação, classificação e sumarização de descrições textuais de consumidores sobre produtos e serviços. Este trabalho apresenta uma análise comparativa entre as principais abordagens usadas na tarefa de Extração de Aspectos em comentários sobre produtos e serviços em web sites. Neste artigo foram implementadas adaptações de quatro métodos de extração de aspectos e avaliados em dois Corpora distintos: um em português e outro em inglês. Nos experimentos realizados foi observado que o método usando aprendizagem supervisionada (redes neurais convolucionais) obteve melhores resultados que os demais.

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Biografia do Autor

Raimundo Santos Moura, Universidade Federal do Piauí

Tem experiência na área de Ciência da Computação, com ênfase em Linguagens de Programação/Compiladores e no Processamento de Linguagens Naturais (PLN), atuando principalmente no tema: mineração de opiniões em descrições textuais.

João Paulo Albuquerque Vieira, Universidade Federal do Piauí

Doutorando em Ciência da Computação e Matemática Computacional pela Universidade de São Paulo, Mestre em Ciência da Computação pela Universidade Federal do Piauí (2018) e Bacharel em Ciência da Computação também pela Universidade Federal do Piauí (2015). Atuou no Laboratório de Processamento de Linguagem Natural (LPLN) desenvolvendo pesquisas relacionadas à Mineração de Opinião. Atualmente é pesquisador no Núcleo Interinstitucional de Linguística Computacional (NILC).

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Publicado

2020-06-18

Como Citar

Moura, R. S., & Vieira, J. P. A. (2020). Análise de Métodos de Extração de Aspectos em Opiniões Regulares. ISys - Brazilian Journal of Information Systems, 13(3), 82–97. Recuperado de https://seer.unirio.br/isys/article/view/9584

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