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Theatrical Genre Prediction using Social Network Metrics

Topics: Clustering and Classification Methods; Concept Mining; Context Discovery; Data Analytics; Data Reduction and Quality Assessment; Information Extraction; Interactive and Online Data Mining; Machine Learning; Mining Text and Semi-Structured Data; Process Mining; Structured Data Analysis and Statistical Methods

Authors: Manisha Shukla 1 ; Susan Gauch 1 and Lawrence Evalyn 2

Affiliations: 1 Department of Computer Science and Engineering, University of Arkansas, Fayetteville, AR and U.S.A. ; 2 Department of English, University of Toronto, Toronto, ON and Canada

ISBN: 978-989-758-330-8

Keyword(s): Social Networks, Genre Prediction, Relationship Mining, Social Network Analysis, Network Theory.

Abstract: With the emergence of digitization, large text corpora are now available online which provide humanities scholars an opportunity to perform literary analysis leveraging the use of computational techniques. Almost no work has been done to study the ability of mathematical properties of network graphs to predict literary features. In this paper, we apply network theory concepts in the field of literature to explore correlations between the mathematical properties of the social networks of plays and the plays’ dramatic genre. Our goal is to find metrics which can distinguish between theatrical genres without needing to consider the specific vocabulary of the play. We generated character interaction networks of 36 Shakespeare plays and tried to differentiate plays based on social network features captured by the character network of each play. We were able to successfully predict the genre of Shakespeare’s plays with the help of social network metrics and hence establish that differences of dramatic genre are successfully captured by the local and global social network metrics of the plays. Since the technique is highly extensible, future work can be applied larger groups of plays, including plays written by different authors, from different periods, or even in different languages. (More)

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Paper citation in several formats:
Shukla, M.; Gauch, S. and Evalyn, L. (2018). Theatrical Genre Prediction using Social Network Metrics.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 229-236. DOI: 10.5220/0006935002290236

@conference{kdir18,
author={Manisha Shukla. and Susan Gauch. and Lawrence Evalyn.},
title={Theatrical Genre Prediction using Social Network Metrics},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2018},
pages={229-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006935002290236},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Theatrical Genre Prediction using Social Network Metrics
SN - 978-989-758-330-8
AU - Shukla, M.
AU - Gauch, S.
AU - Evalyn, L.
PY - 2018
SP - 229
EP - 236
DO - 10.5220/0006935002290236

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