--- license: mit --- # 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 | --- ### 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. ``` @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} } ```