Irony detection in Spanish

robertuito-irony

Repository: https://github.com/pysentimiento/pysentimiento/

Model trained with IRosVA 2019 dataset for irony detection. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.

The positive class marks irony, the negative class marks not irony.

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}
}