Paragraph-based complex networks: application to document classification and authenticity verification
This work addresses text classification and authenticity verification for researchers and practitioners dealing with large volumes of online texts, though it appears incremental as it builds on existing network models.
The study tackled the problem of representing texts for classification and authenticity verification by introducing a novel paragraph-based network that captures semantic similarity, finding that real texts form communities which help discriminate them from artificial ones and that the method can capture semantic features unlike traditional co-occurrence networks, with application to the Voynich manuscript showing compatibility with natural languages.
With the increasing number of texts made available on the Internet, many applications have relied on text mining tools to tackle a diversity of problems. A relevant model to represent texts is the so-called word adjacency (co-occurrence) representation, which is known to capture mainly syntactical features of texts.In this study, we introduce a novel network representation that considers the semantic similarity between paragraphs. Two main properties of paragraph networks are considered: (i) their ability to incorporate characteristics that can discriminate real from artificial, shuffled manuscripts and (ii) their ability to capture syntactical and semantic textual features. Our results revealed that real texts are organized into communities, which turned out to be an important feature for discriminating them from artificial texts. Interestingly, we have also found that, differently from traditional co-occurrence networks, the adopted representation is able to capture semantic features. Additionally, the proposed framework was employed to analyze the Voynich manuscript, which was found to be compatible with texts written in natural languages. Taken together, our findings suggest that the proposed methodology can be combined with traditional network models to improve text classification tasks.