---
language:
- en
tags:
- legal
- finance
- climate
- social
pretty_name: 'CoCoHD: Congress Committee Hearing Dataset - Transcripts'
size_categories:
- 10K<n<100K
license: cc
---

## Dataset Summary

The Congress Committee Hearing Dataset (CoCoHD) Transcripts comprises transcripts from congressional (House and Senate) hearings from 1997 to 2024. 

This dataset is designed to facilitate research in natural language processing (NLP). It contains 30k+ U.S. congressional hearing transcripts from the 105th Congress to the 118th. 

## Dataset Structure

The hearing transcripts of each Congress session are kept in a folder and in text format. 

### Related Datasets

- [CoCoHD Hearing Details](https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details): This dataset provides comprehensive metadata for each congressional hearing, including information such as the hearing title, date, committee, and witnesses.

- [CoCoHD Hearing Details Cleaned](https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details_cleaned): A refined version of the hearing details dataset, this collection has been processed to correct inconsistencies, standardize committee names, and remove duplicate or erroneous records, ensuring higher data quality for analysis.

These datasets offer valuable metadata that complements the CoCoHD transcripts, enabling more detailed and accurate analyses of congressional hearings. 

## Licensing

The CoCoHD Transcripts dataset is released under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/). This permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

## Citation

If you utilize this dataset in your research, please cite it as follows:

```
@inproceedings{hiray-etal-2024-cocohd,
    title = "{C}o{C}o{HD}: Congress Committee Hearing Dataset",
    author = "Hiray, Arnav  and
      Liu, Yunsong  and
      Song, Mingxiao  and
      Shah, Agam  and
      Chava, Sudheer",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.911",
    doi = "10.18653/v1/2024.findings-emnlp.911",
    pages = "15529--15542",
    abstract = "U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the **Co**ngress **Co**mmittee **H**earing **D**ataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee{'}s stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.",
}
```

## GitHub Link
 - [Link to our GitHub repository.](https://github.com/gtfintechlab/CoCoHD)


## Contact Information

Please contact Agam Shah (ashah482[at]gatech[dot]edu) for any issues and questions.