Related papers: A Weak Supervised Dataset of Fine-Grained Emotions in Portuguese

A Weak Supervised Dataset of Fine-Grained Emotions in Portuguese

URL: http://arxiv.org/abs/2108.07638v1
Date: Tue, 17 Aug 2021 14:08:23 GMT
Title: A Weak Supervised Dataset of Fine-Grained Emotions in Portuguese
Authors: Diogo Cortiz, Jefferson O. Silva, Newton Calegari, Ana Lu\'isa Freitas, Ana Ang\'elica Soares, Carolina Botelho, Gabriel Gaudencio R\^ego, Waldir Sampaio, Paulo Sergio Boggio
Abstract summary: This research describes an approach to create a lexical-based weak supervised corpus for fine-grained emotion in Portuguese. Our results suggest lexical-based weak supervision as an appropriate strategy for initial work in low resources environment.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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 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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