Semiodiscursive analysis of TV newscasts based on data mining and image processing

Felipe Leandro Andrade da Conceição, Flávio Luis Cardeal Pádua, Adriano César Machado Pereira, Guilherme Tavares de Assis, Giani David Silva, Antônio Augusto Braighi Andrade

Resumo


This work addresses the development of a novel computer-aided methodology for discourse analysis of TV newscasts. A TV newscast constitutes a particular type of discourse and has become a central part of the modern-day lives of millions of people. It is important to understand how this media content works and how it affects human life. To support the study of TV newscasts under the discourse analysis perspective, this work proposes a newscast structure to recover its main units and extract relevant data, named here as newscast discursive metadata (NDM). The NDM describes aspects, such as screen time and field size of newscasts’ participants and themes addressed. Data mining and image analysis methods are used to extract and analyze the NDM of a dataset containing 41 editions of two Brazilian newscasts. The experimental results are promising, demonstrating the effectiveness of the proposed methodology.

 


Palavras-chave


Journalism; Computing; Discursive Metadata; Discourse Analysis

Texto completo:

PDF (English) (baixado


DOI: http://dx.doi.org/10.4025/actascitechnol.v39i3.29763





ISSN 1806-2563 (impresso) e ISSN 1807-8664 (on-line) e-mail: actatech@uem.br

  

Resultado de imagem para CC BY