Sentiment Analysis is the computer science field that comprises techniques that aim to automatically extract opinions from texts. Usually, these techniques assign a Sentiment Orientation to the whole document (Document Level Sentiment Analysis). But a document can express sentiment about several aspects of an entity. Methods that extract those aspects, paired with the sentiment about them, operate in the Aspect Level. Aspect-Based Sentiment Analysis approaches can be split into two stages: Aspect Extraction and Aspect Sentiment Classification. The literature presents works mainly focused on reviews about hotels, smartphones, or restaurants. In this work, we present an approach for Aspect Extraction based on Multilingual (Google's) and Portuguese (BERTimbau) BERT pre-trained models. Our experiments show that Aspect Extraction based on BERT pre-trained for Portuguese achieved Balanced Accuracy of up to 93% on a corpus of reviews about the accommodation sector.

Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing (NLP) task that extracts referred aspects from text and assigns polarities to opinions about those aspects. Most research on ABSA focuses on English. Only a few ABSA works deal with the Portuguese language. In this work, we used BERTimbau to create a Question-Answer approach to ABSA in Portuguese. First, we post-trained this model with text from the same domain as our target corpus. Then, we constructed an auxiliary sentence from the aspect and converted ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). Our experiments show that ABSA based on BERT for Portuguese achieved Balanced Accuracy (BACC) of 77% on a corpus of reviews about the accommodation sector using a post-trained model with QA approach.