Popular mobile applications receive millions of user reviews. These reviews contain relevant information for software maintenance, such as bug reports and improvement suggestions. The review’s information is a valuable knowledge source for software requirements engineering since the apps review analysis helps make strategic decisions to improve the app quality.

O trabalho propõe o método SVAE, um Variational Autoencoder Semântico que representa os eventos. O método é formado por: (i) um modelo de linguagem dependente de contexto Bidiretional Encoder From Transformers (BERT) (considerado um dos estado-da-arte para tarefas envolvendo texto) que captura características sintáticas e semânticas dos textos; e (ii) um Variational Autoencoder, considerado um dos estado-da-arte para aprendizado de representação.

Neste artigo, propomos uma abordagem para a extração de aspectos em um cenário de transferência de conhecimento multi-domínio, aproveitando assim os dados rotulados de diferentes domínios (origem) para extrair aspectos de um novo domínio não rotulado (destino)

This paper addresses the dependence issues inherent from spatial data by proposing a novel graph-based Spatial Cross Validation approach designed to assess models learned with selected features from spatially contextualized electoral datasets.

This paper proposes a simple data science pipeline to analyze Brazilian elections at the municipal level, in the spatial and temporal dimensions.

O aprendizado com exemplos positivos e não rotulados (do Inglês Positive and Unlabeled Learning - PUL) permite identificar em um conjuntos de exemplos não rotulados os documentos positivos (os quais o usuário rotulou ou tem interesse) e os exemplos negativos. Além disso, uma vez identificados documentos positivos e negativos, pode-se induzir modelos de classificação para classificar exemplos não vistos.

Multilevel optimization aims at reducing the cost of executing a target network-based algorithm by exploiting coarsened, i.e., reduced or simplified, versions of the network. There is a growing interest in multilevel algorithms in networked systems, mostly motivated by the urge for solutions capable of handling large-scale networks. We believe our survey provides a useful resource to individuals interested in learning about multilevel strategies, as well as to those engaged in advancing theoretical and practical aspects of the method or in developing solutions in novel application domains.

Accurate semantic representation models are essential in text mining applications. For a successful application of the text mining process, the text representation adopted must keep the interesting patterns to be discovered. Although competitive results for automatic text classification may be achieved with traditional bag of words, such representation model cannot provide satisfactory classification performances on hard settings where richer text representations are required. In this paper, we present an approach to represent document collections based on embedded representations of words and word senses.

Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze consumer reviews at a high level of detail, where the opinion about each feature of the product or service is considered. ABSA usually explores supervised inductive learning algorithms, which requires intense human effort for the labeling process. In this paper, we investigate Cross-Domain Transfer Learning approaches, in which aspects already labeled in some domains can be used to support the aspect extraction of another domain where there are no labeled aspects.