# Sentiment Analysis in Spanish
## robertuito-sentiment-analysis

Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)



Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model trained in Spanish.

Contains the six Ekman emotions plus a neutral class:

- anger
- disgust
- fear
- joy
- sadness


## Results

Results for the four tasks evaluated in `pysentimiento`. Results are expressed as Macro F1 scores


| model         | emotion       | hate_speech   | irony         | sentiment     |
|:--------------|:--------------|:--------------|:--------------|:--------------|
| robertuito    | 0.560 ± 0.010 | 0.759 ± 0.007 | 0.739 ± 0.005 | 0.705 ± 0.003 |
| roberta       | 0.527 ± 0.015 | 0.741 ± 0.012 | 0.721 ± 0.008 | 0.670 ± 0.006 |
| bertin        | 0.524 ± 0.007 | 0.738 ± 0.007 | 0.713 ± 0.012 | 0.666 ± 0.005 |
| beto_uncased  | 0.532 ± 0.012 | 0.727 ± 0.016 | 0.701 ± 0.007 | 0.651 ± 0.006 |
| beto_cased    | 0.516 ± 0.012 | 0.724 ± 0.012 | 0.705 ± 0.009 | 0.662 ± 0.005 |
| mbert_uncased | 0.493 ± 0.010 | 0.718 ± 0.011 | 0.681 ± 0.010 | 0.617 ± 0.003 |
| biGRU         | 0.264 ± 0.007 | 0.592 ± 0.018 | 0.631 ± 0.011 | 0.585 ± 0.011 |


Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B (HS+TR+AG detection)

## Citation

If you use this model in your research, please cite pysentimiento and RoBERTuito papers:

```
@misc{perez2021pysentimiento,
      title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
      author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
      year={2021},
      eprint={2106.09462},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{perez2021robertuito,
      title={RoBERTuito: a pre-trained language model for social media text in Spanish},
      author={Juan Manuel Pérez and Damián A. Furman and Laura Alonso Alemany and Franco Luque},
      year={2021},
      eprint={2111.09453},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```