A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese
Abstract
Affective Computing is the study of how computers can recognize, interpret and simulate human affects. Sentiment Analysis is a common task in NLP related to this topic, but it focuses only on emotion valence (positive, negative, neutral). An emerging approach in NLP is Emotion Recognition, which relies on fined-grained classification. This research describes an approach to create a lexical-based weakly supervised corpus for fine-grained emotion in Portuguese. We evaluate our dataset by fine-tuning a transformer-based language model (BERT) and validating it on a Gold Standard annotated validation set. Our results (F1-score= .64) suggest lexical-based weak supervision as an appropriate strategy for initial work in low resourced environment.
References
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Demszky, D., Movshovitz-Attias, D., Ko, J., Cowen, A., Nemade, G., and Ravi, S. (2020). GoEmotions: A Dataset of Fine-Grained Emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4040–4054, Stroudsburg, PA, USA. Association for Computational Linguistics.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
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Pereira, D. A. (2021). A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review, 54(2):1087–1115.
Rosalind, P. (2000). Affective Computing. MIT Press, Cambridge.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: Pretrained BERT Models for Brazilian Portuguese. pages 403–417.
Batbaatar, E., Li, M., and Ryu, K. H. (2019). Semantic-Emotion Neural Network for Emotion Recognition From Text. IEEE Access, 7:111866–111878.
Cowen, A. S. and Keltner, D. (2021). Semantic Space Theory: A Computational Approach to Emotion. Trends in Cognitive Sciences, 25(2):124–136.
Demszky, D., Movshovitz-Attias, D., Ko, J., Cowen, A., Nemade, G., and Ravi, S. (2020). GoEmotions: A Dataset of Fine-Grained Emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4040–4054, Stroudsburg, PA, USA. Association for Computational Linguistics.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
Drus, Z. and Khalid, H. (2019). Sentiment Analysis in Social Media and Its Application: Systematic Literature Review. Procedia Computer Science, 161:707–714.
Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3-4):169–200.
Pereira, D. A. (2021). A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review, 54(2):1087–1115.
Rosalind, P. (2000). Affective Computing. MIT Press, Cambridge.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: Pretrained BERT Models for Brazilian Portuguese. pages 403–417.
Published
2021-11-29
How to Cite
CORTIZ, Diogo et al.
A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese. In: BRAZILIAN SYMPOSIUM IN INFORMATION AND HUMAN LANGUAGE TECHNOLOGY (STIL), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 73-81.
DOI: https://doi.org/10.5753/stil.2021.17786.
