README.md

metadata

license: mit
language:
  - en
tags:
  - text-classification
  - social-science
  - politics
  - sentiment-analysis
  - benchmark
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
  - sentiment-classification
annotations_creators:
  - human-annotated
multilinguality: monolingual

Automated Nonparametric Content Analysis Datasets

This repository provides the four benchmark datasets used in:

Connor T. Jerzak, Gary King, and Anton Strezhnev. An Improved Method of Automated Nonparametric Content Analysis for Social Science. Political Analysis, 31(1): 42–58, 2023.

Each dataset is formatted for easy loading in Python and R (CSV). Labels are integer-coded from 1,...,K; text is provided as raw strings.

Datasets

Name	Documents	Categories	Source & Description
enron.csv	1,426	5	Corporate emails from the Enron corpus, hand-coded into five thematic categories (e.g., business, personal, legal)
immigration.csv	462	5	Newspaper editorials on immigration policy, hand-coded into five sentiment/policy categories; originally used in Hopkins & King (2010) and Jerzak et al. (2023)
clinton.csv	1,938	7	Blog posts about Hillary Clinton from 2008, hand-coded into seven topical categories; feature space of ~3,623 word stems
stanford.csv	11,855	5	Sentences from the Stanford Sentiment Treebank, labeled on a five-point sentiment scale; commonly used in text quantification research

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Citation

Connor T. Jerzak, Gary King, Anton Strezhnev. An Improved Method of Automated Nonparametric Content Analysis for Social Science. Political Analysis, 31(1): 42–58, 2023. [PDF]

@article{JSK-readme2,
  title={An Improved Method of Automated Nonparametric Content Analysis for Social Science},
  author={Jerzak, Connor T. and Gary King and Anton Strezhnev},
  journal={Political Analysis},
  year={2023},
  volume={31},
  number={1},
  pages={42-58}
}