Automatic classification of journalistic documents on the Internet

Abstract

Online journalism is increasing every day. There are many news agencies, newspapers, and magazines using digital publication
in the global network. Documents published online are available to users, who use search engines to find them. In order to
deliver documents that are relevant to the search, they must be indexed and classified. Due to the vast number of documents
published online every day, a lot of research has been carried out to find ways to facilitate automatic document classification. The
objective of the present study is to describe an experimental approach for the automatic classification of journalistic documents
published on the Internet using the Vector Space Model for document representation. The model was tested based on a real
journalism database, using algorithms that have been widely reported in the literature. This article also describes the metrics
used to assess the performance of these algorithms and their required configurations. The results obtained show the efficiency
of the method used and justify further research to find ways to facilitate the automatic classification of documents.