Datasets:
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license: mit
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---
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license: mit
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---
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# Automated Nonparametric Content Analysis Datasets
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This repository provides the four benchmark datasets used in:
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> 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.
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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.
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## Datasets
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| Name | Documents | Categories | Source & Description |
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| --------------- | --------: | ---------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| **enron.csv** | 1,426 | 5 | Corporate emails from the Enron corpus, hand-coded into five thematic categories (e.g., business, personal, legal) |
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| **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) |
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| **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 |
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| **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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---
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### Citation
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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.
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```
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@article{JSK-readme2,
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title={An Improved Method of Automated Nonparametric Content Analysis for Social Science},
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author={Jerzak, Connor T. and Gary King and Anton Strezhnev},
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journal={Political Analysis},
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year={2023},
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volume={31},
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number={1},
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pages={42-58}
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}
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```
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