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Corpus-based dictionaries for sentiment analysis of specialized vocabularies

Published online by Cambridge University Press:  02 April 2019

Douglas R. Rice*
Affiliation:
Department of Political Science, University of Massachusetts, Amherst, Massachusetts, United States
Christopher Zorn
Affiliation:
Department of Political Science, Pennsylvania State University, University Park, PennsylvaniaUnited States
*
*Corresponding author. Email: drrice@umass.edu

Abstract

Contemporary dictionary-based approaches to sentiment analysis exhibit serious validity problems when applied to specialized vocabularies, but human-coded dictionaries for such applications are often labor-intensive and inefficient to develop. We demonstrate the validity of “minimally-supervised” approaches for the creation of a sentiment dictionary from a corpus of text drawn from a specialized vocabulary. We demonstrate the validity of this approach in estimating sentiment from texts in a large-scale benchmarking dataset recently introduced in computational linguistics, and demonstrate the improvements in accuracy of our approach over well-known standard (nonspecialized) sentiment dictionaries. Finally, we show the usefulness of our approach in an application to the specialized language used in US federal appellate court decisions.

Type
Original Article
Copyright
Copyright © The European Political Science Association 2019

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Footnotes

All materials necessary to replicate the results reported herein are posted to the Political Science Research and Methods Dataverse.

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