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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  and
Christopher Zorn
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, Pennsylvania United States
*
*Corresponding author. Email: drrice@umass.edu
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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.

Keywords

Text and content analysis

Information

Type
Original Article
Information
Political Science Research and Methods , Volume 9 , Issue 1 , January 2021 , pp. 20 - 35
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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Supplementary material: Link

Rice and Zorn Dataset

Dataset

https://doi.org/10.7910/DVN/4EKHFM
Link
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