Using Data Augmentation and Neural Networks to Improve the Emotion Analysis of Brazilian Portuguese Texts

Information and Communication Technologies present as an interesting alternative for the mitigation of barriers that arise in the context of communication of information, mainly as technologies aimed at the machine translation of content in oral language into sign language. After years, despite the improvement of these technologies, the use of them still divides the opinions of the Deaf Community, due to the low emotional expressiveness of 3D avatars. Therefore, as a way to assist the machine translation of texts in oral language to sign language, this study aims to evaluate the influence of the parameters of a data augmentation method in a textual dataset and the use of neural networks for emotion analysis of Bazilian Portuguese texts. The analysis of emotions in texts presents a relevant challenge in diversity due to the nuances and different forms of expression that the human language uses. In this context, the use of deep neural networks has gained enough space as a way to deal with these challenges, mainly with the use of algorithms that deal with emotion analysis as a textual classification task, such as the MultiFiT approach. To circumvent the scarcity of data in Brazilian Portuguese aimed at this task, some strategies for increasing data were evaluated and applied to improve the database used in training. The results of the emotion analysis experiments with Transfer Learning pointed to accuracy above 94% in the best case.