Classifying informative and imaginative prose using complex networks

TL;DR

This study demonstrates that analyzing the structural properties of texts using complex network models can effectively distinguish between informative and imaginative prose with high accuracy.

Contribution

The paper introduces a novel approach employing complex network features derived from function words to classify texts, achieving superior accuracy over existing methods.

Findings

Achieved up to 95% classification accuracy.

Symmetry and accessibility are key network features for text classification.

Structural text features complement semantic analysis in language tasks.

Abstract

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, which encompasses machine translation, automatic summarization and document classification. In the latter, many approaches have emphasized the semantical content of texts, as it is the case of bag-of-word language models. This approach has certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only on a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterizing texts.