Text structuring methods based on complex network: a systematic review

Currently, there is a large amount of text being shared through the Internet. These texts are available in different forms-structured, unstructured and semi structured. There are different ways of analyzing texts, but domain experts usually divide this process in some steps such as pre-processing, feature extraction and a final step that could be classification, clustering, summarization, and keyword extraction, depending on the purpose over the text. For this processing, several approaches have been proposed in the literature based on variations of methods like artificial neural network and deep learning. In this paper, we conducted a systematic review of papers dealing with the use of complex networks approaches for the process of analyzing text. The main results showed that complex network topological properties, measures and modeling can capture and identify text structures concerning different purposes such as text analysis, classification, topic and keyword extraction, and summarization. We conclude that complex network topological properties provide promising strategies with respect of processing texts, considering their different aspects and structures.

Automated summary

Text analysis involves processing texts in various forms, typically including preprocessing, feature extraction, and classification. Research suggests that complex network topological properties can effectively capture and identify text structures, facilitating text analysis and classification for different purposes.