  Human communication is often executed in the form of a narrative, an account
of connected events composed of characters, actions, and settings. A coherent
narrative structure is therefore a requisite for a well-formulated narrative --
be it fictional or nonfictional -- for informative and effective communication,
opening up the possibility of a deeper understanding of a narrative by studying
its structural properties. In this paper we present a network-based framework
for modeling and analyzing the structure of a narrative, which is further
expanded by incorporating methods from computational linguistics to utilize the
narrative text. Modeling a narrative as a dynamically unfolding system, we
characterize its progression via the growth patterns of the character network,
and use sentiment analysis and topic modeling to represent the actual content
of the narrative in the form of interaction maps between characters with
associated sentiment values and keywords. This is a network framework advanced
beyond the simple occurrence-based one most often used until now, allowing one
to utilize the unique characteristics of a given narrative to a high degree.
Given the ubiquity and importance of narratives, such advanced network-based
representation and analysis framework may lead to a more systematic modeling
and understanding of narratives for social interactions, expression of human
sentiments, and communication.
