IMFBERT is built by fine-tuning the siebert/sentiment-roberta-large-english model with IMF (International Monetary Fund) Executive Board meeting minutes (around 150,000 sentences). This model is suitable for English. Labels in this model are:

1 : Positive
0 : Negative

Example Usage

from transformers import pipeline
sentiment_classification = pipeline(task = 'sentiment-analysis', model = 'faycadnz/IMFBERT_binary')
sentiment_classification('They remain vulnerable to external shocks.')

Citation

If you find this repository useful in your research, please cite the following paper:

APA format:

Deniz, A., Angin, M., & Angin, P. (2022, May). Understanding IMF Decision-Making with Sentiment Analysis. In 2022 30th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.

Bibtex format:

@inproceedings{deniz2022understanding,
  title={Understanding IMF Decision-Making with Sentiment Analysis},
  author={Deniz, Ay{\c{c}}a and Angin, Merih and Angin, Pelin},
  booktitle={2022 30th Signal Processing and Communications Applications Conference (SIU)},
  pages={1--4},
  year={2022},
  organization={IEEE}
}