Text Analysis Using Different Graph-Based Representations

This paper presents an overview of different graph-based representations proposed to solve text classification tasks. The core of this manuscript is to highlight the importance of enriched/non-enriched co-occurrence graphs as an alternative to traditional features representation models like vector representation, where most of the time these models can not map all the richness of text documents that comes from the web (social media, blogs, personal web pages, news, etc). For each text classification task the type of graph created as well as the benefits of using it are presented and discussed. In specific, the type of features/patterns extracted, the implemented classification/similarity methods and the results obtained in datasets are explained. The theoretical and practical implications of using co-occurrence graphs are also discussed, pointing out the contributions and challenges of modeling text document as graphs.