## 1 Introduction
[7] provided a brief overview of the most relevant techniques in NLP tasks, highlighting that SA models are becoming increasingly popular in using DL techniques, with the current state-of-the-art model being an ensemble model that includes convolutional neural networks (CNNs) and long short-term memory (LSTM). [PERSON] et al. [3], in conformity with [PERSON] et al. [7], stated that artificial neural networks (ANNs) are preferred by researchers in most NLP tasks, with the primary task being text representation in a vectorized dense form. LSTM- and BiLSTM-based models are the latest approaches preferred over CNNs. Additionally, the development of pre-trained models such as BERT, a transformer language model (TLM), allowed fine-tuning for tasks such as SA or question answering (QA) using transfer learning (TL) techniques [3; 10]. Finally, [PERSON] et al. [8] discussed in their most recent survey the most relevant datasets and metrics used for the evaluation of SA models. They found that researchers prefer to use domain-specific lexicons to solve SA problems, and conventional machine learning (ML) and DL only when they need to overcome limitations from the previous model. The most relevant architectures found in recent studies are constituted by LSTM, BiLSTM, CNN, or transformer layers, which benefit from high accuracy by mapping long-term dependencies and focusing on the most relevant terms of the input sequence [11; 12]. Some researchers have made efforts to train this model using large datasets from other languages, emphasizing the Portuguese variations of interest in this study, such as BERTimbau [13] and Albertina PT-* [14]. The lack of studies in languages other than English was also highlighted by [PERSON] et al. [3] and is proposed for future work. The central studies that introduce and characterize the landscape and developments on SA models for the Portuguese language were driven by [PERSON] [9] and [PERSON] et al. [15]. The former performed a systematic mapping review up to 2014 and discovered that almost all available language resources were from Brazilian and European Portuguese variations. The encountered studies mainly investigated sentiment classification at the document level and used logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) models with at least one type of text pre-processing task. The latter made a survey of SA studies in Portuguese, mainly from Portugal and Brazil, which stated the need for new proposals of models for the Portuguese language to better tackle the language specificities. The survey analyzed the whole spectrum of solutions, including lexicon-based approaches, classical ML approaches, and DL approaches, which incorporate CNNs, LSTMs, and transformers. [PERSON] and [PERSON] [16] reunited five known Brazilian datasets and analyzed them in terms of size, number of n-grams, and length of reviews. They concluded that these Portuguese datasets are less rich in vocabulary than English datasets. Therefore, complex algorithms for Portuguese datasets may not provide the same performance gain when compared to English datasets. Additionally, [PERSON] and [PERSON] [17] introduced the Brazilian dataset TweetSentBR, which was manually labeled as positive, negative, and neutral by seven native speakers of Brazilian Portuguese. [PERSON] et al. [18] introduced the dataset BRTweetSentCorpus, manually labeled as positive, negative, ambiguous, and non-opinionated. This process is considerably time-consuming and is not reasonable for larger datasets. Several studies are based on consumer reviews along with a numerical value indicating a rating evaluation between 1 and 5 stars. [PERSON] and [PERSON] [19] presented an empirical foundation for this heuristic, applying a t-Student test to verify the comparability of the star rating with a manual classification performed by a human. The result indicated that the star rating is equivalent to manual classification, allowing reliable usage for training an ML algorithm.