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Extracting Social Networks from Literary Fiction

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.

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Journal ArticleDOI

Clustering of Novels Represented as Social Networks

TL;DR: This paper builds static and dynamic social networks of characters as a strategy to represent the narrative structure of novels in a quantifiable manner and performs clustering on the vectors and analyzes the resulting clusters in terms of genre and authorship.
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A Dutch coreference resolution system with an evaluation on literary fiction

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Towards a computational approach to literary text analysis

TL;DR: Several types of literary-theoretic approaches to literary text analysis and several concepts from Computational Linguistics and Artificial Intelligence that could be used to model and support them are considered.
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Study of linguistic features incorporated in a literary book recommender system

TL;DR: Two recommendation algorithms were trained on more than a hundred features learned from the full text of the books to generate more accurate lists of suggested books than many competitive recommender systems.
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Subgroup Detector: A System for Detecting Subgroups in Online Discussions

TL;DR: Subgroup Detector is presented, a system for analyzing threaded discussions and identifying the attitude of discussants towards one another and towards the discussion topic using an unsupervised approach based on rule-based opinion target detecting and un supervised clustering techniques.
References
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Proceedings ArticleDOI

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Proceedings Article

The Automatic Content Extraction (ACE) Program Tasks, Data, and Evaluation

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Graphs, Maps, Trees: Abstract Models for a Literary History

TL;DR: MoreMoretti as discussed by the authors argues that literature scholars should stop reading books and start counting, graphing, and mapping them instead, and offers charts, maps and time lines, developing the idea of "distant reading" into a full-blown experiment in literary history.