Extracting Social Networks from Literary Fiction
David K. Elson,Nicholas Dames,Kathleen R. McKeown +2 more
- pp 138-147
TLDR
The method involves character name chunking, quoted speech attribution and conversation detection given the set of quotes, which provides evidence that the majority of novels in this time period do not fit two characterizations provided by literacy scholars.Abstract:
We present a method for extracting social networks from literature, namely, nineteenth-century British novels and serials. We derive the networks from dialogue interactions, and thus our method depends on the ability to determine when two characters are in conversation. Our approach involves character name chunking, quoted speech attribution and conversation detection given the set of quotes. We extract features from the social networks and examine their correlation with one another, as well as with metadata such as the novel's setting. Our results provide evidence that the majority of novels in this time period do not fit two characterizations provided by literacy scholars. Instead, our results suggest an alternative explanation for differences in social networks.read more
Citations
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Book ChapterDOI
Analysis of Structure and Plots of Characters from Plays and Novels to Create Novel and Plot Data Bank (NPDB)
TL;DR: In this article, a novel and plot data bank (NPDB) is proposed to store the relevant information by computing informative properties of the resulting network, including the leading characters along with their gender.
Journal ArticleDOI
Grounding Characters and Places in Narrative Texts
TL;DR: This paper proposed a new spatial relationship categorization task to assign a spatial relationship category for every character and location co-mention within a window of text, taking into consideration linguistic context, narrative tense, and temporal scope.
Peer Review
Decoding the Popularity of TV Series: A Network Analysis Perspective
TL;DR: In this article , the authors analyze the character networks extracted from three popular television series and explore the relationship between a TV show episode's character network metrics and its review from IMDB, finding that certain network metrics of character interactions in episodes have a strong correlation with the review score of TV series.
Proceedings Article
Grounding Characters and Places in Narrative Text
TL;DR: The authors proposed a new spatial relationship categorization task to assign a spatial relationship category for every character and location co-mention within a window of text, taking into consideration linguistic context, narrative tense, and temporal scope.
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