Search (3 results, page 1 of 1)

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Abstract

In traditional classification, fictional works are indexed only by their form, genre, and language, while their subject content is believed to be irrelevant. However, recent research suggests that this may not be the best approach. We tested indexing of a small sample of selected fictional works by Integrative Levels Classification (ILC2), a freely faceted system based on phenomena instead of disciplines and considered the structure of the resulting classmarks. Issues in the process of subject analysis, such as selection of relevant vs. non-relevant themes and citation order of relevant ones, are identified and discussed. Some phenomena that are covered in scholarly literature can also be identified as relevant themes in fictional literature and expressed in classmarks. This can allow for hybrid search and retrieval systems covering both fiction and nonfiction, which will result in better leveraging of the knowledge contained in fictional works.

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Abstract

This study examines subject and genre representation in metadata that describes information resources created for children and young adult audiences. Both quantitative and limited qualitative analyses were applied to the analysis of WorldCat records collected in 2021 and contributed by the Children's and Young Adults' Cataloging Program at the US Library of Congress. This dataset contains records created several years prior to the data collection point and edited by various OCLC member institutions. Findings provide information on the level and patterns of application of these kinds of metadata important for information access, with a focus on the fields, subfields, and controlled vocabularies used. The discussion of results includes a detailed evaluation of genre and subject metadata quality (accuracy, completeness, and consistency).

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Abstract

Subject headings and genre terms are notoriously difficult to apply, yet are important for fiction. The current project functions as a proof of concept, using a text-mining methodology to identify affective information (emotion and tone) about fiction titles from professional book reviews as a potential first step in automating the subject analysis process. Findings are presented and discussed, comparing results to the range of aboutness and isness information in library cataloging records. The methodology is likewise presented, and how future work might expand on the current project to enhance catalog records through text-mining is explored.