Corpus-based dictionaries for sentiment analysis of specialized vocabularies

Douglas R. Rice, Christopher Zorn

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)20-35
Number of pages16
JournalPolitical Science Research and Methods
Issue number1
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations


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