Sentiment Analysis in Spanish

robertuito-sentiment-analysis

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

Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is 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}
}