Artigo sobre Redes Complexas: 'A Critical Survey of the Multilevel Method in Complex Networks'

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.


Artigo sobre Mineração de Textos: 'Knowledge-enhanced document embeddings for text classification'

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.


Artigo sobre Análise de Sentimentos: 'Cross-domain aspect extraction for sentiment analysis: A transductivelearning approach'

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.

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