  In this study we propose a framework to characterize documents based on their
semantic flow. The proposed framework encompasses a network-based model that
connected sentences based on their semantic similarity. Semantic fields are
detected using standard community detection methods. as the story unfolds,
transitions between semantic fields are represent in Markov networks, which in
turned are characterized via network motifs (subgraphs). Here we show that the
proposed framework can be used to classify books according to their style and
publication dates. Remarkably, even without a systematic optimization of
parameters, philosophy and investigative books were discriminated with an
accuracy rate of 92.5%. Because this model captures semantic features of texts,
it could be used as an additional feature in traditional network-based models
of texts that capture only syntactical/stylistic information, as it is the case
of word adjacency (co-occurrence) networks.
