Uma abordagem em cascata para predição de gênero a partir de textos em Português

• Ano de defesa: 2021
• Autor(a) principal: Morais, João Pedro Moreira de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Ciência da Computação; UFLA; brasil; Departamento de Ciência da Computação
• Palavras-chave em Português: Caracterização autoral; Mineração de texto; Predição de gênero; Língua portuguesa; Author profiling; Portuguese language; Text mining; Gender prediction; Ciência da Computação
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/49191

Author Profiling, whose objective is the analysis of a text to uncover characteristics (e.g., gen- der and age) of its author, has become an important task in different areas such as forensics, marketing, and e-commerce. Although a lot of research has been conducted on this task for some widely used lan- guages (e.g., English), there is still a lot of room for improvement in studies involving the Portuguese language. Thus, this work contributes by proposing and evaluating a cascading approach, which combi- nes a weighted lexical approach, a heuristic and a classifier, for the gender prediction problem using only textual content written in the Portuguese language. The proposed approach takes into account both spe- cificities of the Portuguese language and domain characteristics of the texts. The results obtained from the proposed approach showed that exploring the specificities of the Portuguese language and domain characteristics of the texts can positively contribute to the performance of the gender prediction task.