Complex dynamics of text analysis

This paper presents a novel method for the analysis of nonlinear text quality in Chinese language. Texts produced by university students in China were represented as scale-free networks (word adjacency model), from which typical network features such as the in/outdegree, clustering coefficient and network dynamics were obtained. The method integrates the classical concepts of network feature representation and text quality series variation. The analytical and numerical scheme leads to a parameter space representation that constitutes a valid alternative to represent the network features. The results reveal that complex network features of different text qualities can be clearly revealed and applied to potential applications in other instances of text analysis.