Sentiment analysis for portuguese with BERT models

This paper presents the development and application of the BERT model (Bidirectional Encoder Representations from Transformers) for sentiment analysis in Portuguese texts, focusing on data from social networks. The study is justified by the need to understand the sentimental nuances expressed in a language with limited NLP resources, such as Portuguese, and the relevance of analyzing sentiments on widely used platforms. The objectives center on training and validating a Portuguese-adapted BERT model, using a manually classified dataset, as well as another dataset collected automatically without classification labels. The methodology involved fine-tuning BERT to adjust it to the linguistic peculiarities of Portuguese, followed by rigorous validation that included comparisons with classic NLP algorithms and extrinsic analyses, such as the generation of word clouds to visualize the frequencies of words associated with different sentiments. The results demonstrate an accuracy of 86.64% in the BERTimbau model, which was chosen due to its pre-training in Portuguese and its superior performance in tests, thus evidencing its effectiveness in classification and its relevance for practical applications. It was also observed through word cloud analysis that a large part of the classifications of the Reddit dataset was effective due to the presence of words commonly associated with the sentiments to which they were classified. This work contributes to the field of NLP by providing a Portuguese-adapted model for sentiment analysis, as well as valuable perspectives for future research and applications in various areas, including behavioral analysis and mental health monitoring.