Text Mining and Subject Analysis for Fiction; or, Using Machine Learning and Information Extraction to Assign Subject Headings to Dime Novels

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between...

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Vydáno v:Cataloging & classification quarterly Ročník 57; číslo 5; s. 315 - 336
Hlavní autor: Short, Matthew
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Routledge 04.07.2019
Taylor & Francis Ltd
Témata:
cataloging digital resources
cataloguing popular fiction
Classification
Clustering
Data mining
Digitization
dime novels
Experiments
Extraction
Fiction
Information retrieval
Literary criticism
Machine learning
Novels
Productivity
Researcher subject relations
Subject analysis
Subject heading schemes
Subject headings
text mining
ISSN:0163-9374, 1544-4554
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Abstract This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
AbstractList This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
Author Short, Matthew
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CitedBy_id crossref_primary_10_1080_19386389_2022_2030635
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Copyright 2019 The Author(s). Published with license by Taylor & Francis Group, LLC 2019
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SubjectTerms cataloging digital resources
cataloguing popular fiction
Classification
Clustering
Data mining
Digitization
dime novels
Experiments
Extraction
Fiction
Information retrieval
Literary criticism
Machine learning
Novels
Productivity
Researcher subject relations
Subject analysis
Subject heading schemes
Subject headings
text mining
Title Text Mining and Subject Analysis for Fiction; or, Using Machine Learning and Information Extraction to Assign Subject Headings to Dime Novels
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