A Weak Supervised Dataset of Fine-Grained Emotions in Portuguese
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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 weak 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 Golden Standard annotated validation set. Our results (F1-score= .64) suggest lexical-based weak supervision as an appropriate strategy for initial work in low resources environment.
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