  Text classification is a natural language processing (NLP) task relevant to
many commercial applications, like e-commerce and customer service. Naturally,
classifying such excerpts accurately often represents a challenge, due to
intrinsic language aspects, like irony and nuance. To accomplish this task, one
must provide a robust numerical representation for documents, a process known
as embedding. Embedding represents a key NLP field nowadays, having faced a
significant advance in the last decade, especially after the introduction of
the word-to-vector concept and the popularization of Deep Learning models for
solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent
Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite
the impressive achievements in this field, the literature coverage regarding
generating embeddings for Brazilian Portuguese texts is scarce, especially when
considering commercial user reviews. Therefore, this work aims to provide a
comprehensive experimental study of embedding approaches targeting a binary
sentiment classification of user reviews in Brazilian Portuguese. This study
includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based)
NLP models. The methods are evaluated with five open-source databases with
pre-defined data partitions made available in an open digital repository to
encourage reproducibility. The Fine-tuned TLMs achieved the best results for
all cases, being followed by the Feature-based TLM, LSTM, and CNN, with
alternate ranks, depending on the database under analysis.
