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arXiv · 2108.07638

A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese

Diogo Cortiz·Jefferson O. Silva·Newton Calegari·Ana Luísa Freitas·Ana Angélica Soares·Carolina Botelho·Gabriel Gaudencio Rêgo·Waldir Sampaio·Paulo Sergio Boggio
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Abstract

Affective Computing is the study of how computers can recognize, interpret and simulate human affects. Sentiment Analysis is a common task inNLP 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 evaluated 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.

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Diogo Cortiz, Jefferson O. Silva, Newton Calegari, Ana Luísa Freitas, Ana Angélica Soares, Carolina Botelho, Gabriel Gaudencio Rêgo, Waldir Sampaio, Paulo Sergio Boggio. 2021-08-17. A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese. https://arxiv.org/abs/2108.07638

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