--- language: - en library_name: pysentimiento tags: - twitter - irony --- # Irony detection in English ## bertweet-irony Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/) Model trained with SemEval 2018 dataset Task 3 (Van Hee et all, 2018) for irony detection. Base model is [BERTweet], a RoBERTa model trained in English tweets. The positive class marks irony, the negative class marks not ironic content. ## Results Results for the four tasks evaluated in `pysentimiento`. Results are expressed as Macro F1 scores | Model | sentiment | emotion | hate_speech | irony | |:-----------|:------------|:------------|:--------------|:------------| | bert | 69.6 +- 0.4 | 42.7 +- 0.6 | 56.0 +- 0.8 | 68.1 +- 2.2 | | electra | 70.9 +- 0.4 | 37.2 +- 2.9 | 55.6 +- 0.6 | 71.3 +- 1.8 | | roberta | 70.4 +- 0.3 | 45.0 +- 0.9 | 55.1 +- 0.4 | 70.4 +- 2.9 | | robertuito | 69.6 +- 0.5 | 43.0 +- 3.3 | 57.5 +- 0.2 | 73.9 +- 1.4 | | bertweet | 72.0 +- 0.4 | 43.1 +- 1.8 | 57.7 +- 0.7 | 80.8 +- 0.7 | 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, dataset and pre-trained model papers: ``` @misc{perez2021pysentimiento, title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks}, author={Juan Manuel PĂ©rez and Juan Carlos Giudici and Franco Luque}, year={2021}, eprint={2106.09462}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{van2018semeval, title={Semeval-2018 task 3: Irony detection in english tweets}, author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, pages={39--50}, year={2018} } @inproceedings{nguyen2020bertweet, title={BERTweet: A pre-trained language model for English Tweets}, author={Nguyen, Dat Quoc and Vu, Thanh and Nguyen, Anh Tuan}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, pages={9--14}, year={2020} } ```