TENOR

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Topic Modeling for Content Analysis, including LDA and various neural topic models.

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# TENOR: Efficient Document Labeling/Topic Modeling Tool to Social Scoence Content Analysis
TENOR is a user interface for speeding up document labeling process and reducing the number of documents needed to be labeled. See the paper for details:
- (2024). Beyond Automated Evaluation Metrics: Evaluating Topic Models On Practical Social Science Content Analysis Tasks. EACL. https://arxiv.org/abs/2401.16348

## References

If you find this tool helpful, you can cite the following paper:

```bibtex
@misc{li2024automated,
      title={Beyond Automated Evaluation Metrics: Evaluating Topic Models On Practical Social Science Content Analysis Tasks}, 
      author={Zongxia Li and Andrew Mao and Daniel Stephens and Pranav Goel and Emily Walpole and Alden Dima and Juan Fung and Jordan Boyd-Graber},
      year={2024},
      eprint={2401.16348},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## Getting Started

This tool's frontend code interface is adopted from [this repository](https://github.com/daniel-stephens/community_resilience). To run the app interface locally with the default Bills dataset, follow these steps:

```bash
git clone https://github.com/zli12321/TENOR.git
cd TENOR
pip install -r requirements.txt
```

## Setup

#### 1. Data Preprocessing
Preprocess the data for topic model training. The processed data will be saved to the specified --new_json_path directory
```
./01_data_process.sh
```

#### 2. Training Topic Models

You can obtain trained topic models in two ways:

1. If you're looking to get started quickly without training your own models, we've got you covered. Pre-trained models on the bills dataset, configured with 35 topics, are ready for use. You can download these models directly from [Google Drive](https://drive.google.com/drive/folders/1-k6YcC2KLp8iULGF5zmpAYlpk49dbX4W?usp=sharing)

After downloading, place the model files in the following directory of your local repository:
```
./flask_app/Topic_Models/trained_Models/
```

2. Train Your Own Models

For those who need customized topic models, we provide a convenient script to facilitate the training process:

```bash
./02_train_model.sh
```

This script will lead you through the steps necessary to train new models, which will then be saved to the location specified in the bash script argument `--save_trained_model_path`.

**Configuration Note:**
The application defaults to using 35 topics for model training. If your requirements differ, please follow these steps to ensure compatibility:

1. Execute the model training script with the number of topics set to your preference.
2. Once training is complete, adjust the code in `app.py` to align with your model's topic count. Specifically, change the value on line 128 to correspond with the number of topics in your newly trained model.

#### 3. Starting the Web Application

To launch the web application on your local machine, execute the provided shell script by running the following command in your terminal:

```bash
./03_run_app.sh
```

Upon successful execution of the script, the web application will be available. You can access it by opening your web browser and navigating to:

```
http://localhost:5001
```

If you wish to use a different port than the default `5001`, ensure that the `03_run_app.sh` script is configured accordingly before starting the application.

## Dataset Information

This app supports two datasets:

1. **20newsgroup**: A collection of newsgroup documents.
2. **congressional_bill_project_dataset**: A compilation of Congressional Bill documents.

For the Congressional Bill dataset, the app uses data from these sources:

- [Comparative Agendas Project](https://www.comparativeagendas.net/us)
- [Congressional Bills](http://www.congressionalbills.org)

The original Bills dataset uses numbers as labels. To correlate topics with labels, consult the [Codebook](https://comparativeagendas.s3.amazonaws.com/codebookfiles/Codebook_PAP_2019.pdf).

## Interface Demonstration

<img src="/results/images/list3.png" alt="List Interface" width="900" title="List Interface"/>
 
 The List Interface provides a streamlined overview of documents grouped by topics.

<img src="/results/images/doc3.png" alt="Document Interface" width="900" title="Document Interface"/>
 
 The Document Interface improves reading experience by providing ranked relevant topics and automatic highlights of keywords in the document.

## Acknowledgements

This project was built upon the work of the following repositories:

- [Embedded Topic Model Repository](https://github.com/lffloyd/embedded-topic-model/tree/main)
- [Contextualized Topic Models Repository](https://github.com/MilaNLProc/contextualized-topic-models)
- [BERTopic Repository](https://github.com/MaartenGr/BERTopic/tree/master)
- [tomotopy Repository](https://github.com/bab2min/tomotopy)

We extend our gratitude to the authors of these original repositories for their valuable contributions and inspiration.

## License

This project is licensed under the [MIT License](LICENSE.md) - see the LICENSE file for details.

## Contact

For any additional questions or comments, please contact [zli12321@umd.edu].

Thank you for using or contributing to the Document Annotation NLP Tool!

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