Abstract
Since the 18th century, the novel has been one of the defining forms of English writing, a mainstay of popular entertainment and academic criticism. Despite its importance, however, there are few computational studies of the large-scale structure of novels—and many popular representations for discourse modeling do not work very well for novelistic texts. This paper describes a high-level representation of plot structure which tracks the frequency of mentions of different characters, topics and emotional words over time. The representation can distinguish with high accuracy between real novels and artificially permuted surrogates; characters are important for eliminating random permutations, while topics are effective at distinguishing beginnings from ends.
Highlights
The novel, one of the characteristic forms of modern English literature, poses several interesting challenges from the point of view of computational analysis
It is clear that not all descriptions of sequences of events make for acceptable novelistic plots, but literary theorists have taken a variety of perspectives on what the defining characteristics of plot structure are
Since the results in this paper show that topic model features are useful in capturing a global beginning-to-end temporal structure, evaluating these more complex topic models is a promising direction for future work
Summary
The novel, one of the characteristic forms of modern English literature, poses several interesting challenges from the point of view of computational analysis. Some of these have to do with the sheer size of a novel. The representation is constructed using lexical distribution rather than requiring text analyses (possibly error-prone) with complex NLP tools. This representation is used to create models capable of distinguishing real novels from artificially disordered texts for which plot structure is missing or incomprehensible
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