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Semantically enriched representations for Portuguese textmining: models and applications

Abstract

Text Mining techniques have become essential for supporting text analysis and knowledge discovery as the volume and variety of digital text documents have increased, either in social networks and the Web or inside organizations. Despite the application task or applied technique, the treatment of text semantics is an important challenge of the Text Mining process. The challenge is even bigger when we analyze Portuguese texts due to language particularities and the low number of Portuguese resources and researches. In this context, this project aims to advance Text Mining research, focusing on the Portuguese language, and disseminate the knowledge of the field by applying Text Mining techniques in different real-world problems. We will investigate and propose semantically enriched text representation models, considering both the vector-space model and network-based representations, as well as their application in one-class learning. As a first step to support this research, we will collect, prepare and characterize collections of texts written in Portuguese, and make consolidated information about labeled collections available to the research community. Lastly, we will evaluate and apply semantically enriched text representations in different Text Mining problems, such as sentiment analysis, recommendation systems, fake news detection, literature-based discovery and event mining. (AU)

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VEICULO: TITULO (DATA)

Scientific publications (13)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
RODRIGUES MATTOS, JOAO PEDRO; MARCACINI, RICARDO M.; BAILEY, J; MIETTINEN, P; KOH, YS; TAO, D; WU, X. Semi-Supervised Graph Attention Networks for Event Representation Learning. 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021), v. N/A, p. 6-pg., . (19/25010-5, 19/07665-4)
ALVES DE LIMA, VITOR MESAQUE; DE ARAUJO, ADAILTON FERREIRA; MARCACINI, RICARDO MARCONDES. Temporal dynamics of requirements engineering from mobile app reviews. PEERJ COMPUTER SCIENCE, v. 7, p. 26-pg., . (19/25010-5, 19/07665-4)
STEVE ATAUCURI CRUZ, LORD FLAUBERT; SILVA, DIEGO FURTADO; WANI, MA; SETHI, I; SHI, W; QU, G; RAICU, DS; JIN, R. Financial Time Series Forecasting Enriched with Textual Information. 20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021), v. N/A, p. 6-pg., . (19/25010-5)
ARAUJO, ADAILTON F.; GOLO, MARCOS P. S.; MARCACINI, RICARDO M.. Opinion mining for app reviews: an analysis of textual representation and predictive models. AUTOMATED SOFTWARE ENGINEERING, v. 29, n. 1, . (19/25010-5, 19/07665-4)
DO CARMO, PAULO; MARCACINI, RICARDO; CHEN, Y; LUDWIG, H; TU, Y; FAYYAD, U; ZHU, X; HU, X; BYNA, S; LIU, X; et al. Embedding propagation over heterogeneous event networks for link prediction. 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), v. N/A, p. 10-pg., . (19/25010-5, 19/07665-4)
GONZAGA, VICTOR MACHADO; MURRUGARRA-LLERENA, NILS; MARCACINI, RICARDO; ACM. Multimodal intent classification with incomplete modalities using text embedding propagation. PROCEEDINGS OF THE 27TH BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA '21), v. N/A, p. 4-pg., . (19/07665-4, 19/25010-5)
GOLO, MARCOS P. S.; ARAUJO, ADAILTON F.; ROSSI, RAFAEL G.; MARCACINI, RICARDO M.. Detecting relevant app reviews for software evolution and maintenance through multimodal one-class learning. INFORMATION AND SOFTWARE TECHNOLOGY, v. 151, p. 12-pg., . (19/25010-5)
PEGORARO SANTANA, IGOR ANDRE; DOMINGUES, MARCOS AURELIO. CONTEXT-AWARE MUSIC RECOMMENDATION WITH METADATA AWARENESS AND RECURRENT NEURAL NETWORKS. COMPUTING AND INFORMATICS, v. 41, n. 3, p. 27-pg., . (19/25010-5)
SANTOS, BRUCCE NEVES DOS; MARCACINI, RICARDO MARCONDES; REZENDE, SOLANGE OLIVEIRA. Multi-Domain Aspect Extraction Using Bidirectional Encoder Representations From Transformers. IEEE ACCESS, v. 9, p. 91604-91613, . (19/25010-5, 19/07665-4)
DE MORAES JUNIOR, MARCELO ISAIAS; MARCACINI, RICARDO MARCONDES; IEEE. On the Use of Aggregation Functions for Semi-Supervised Network Embedding. 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, v. N/A, p. 8-pg., . (22/09091-8, 19/25010-5, 19/07665-4)
REIS FILHO, IVAN J.; MARTINS, LUIZ H. D.; PARMEZAN, ANTONIO R. S.; MARCACINI, RICARDO M.; REZENDE, SOLANGE O.; XAVIER-JUNIOR, JC; RIOS, RA. Sequential Short-Text Classification from Multiple Textual Representations with Weak Supervision. INTELLIGENT SYSTEMS, PT I, v. 13653, p. 15-pg., . (19/07665-4, 19/25010-5)
GOLO, MARCOS PAULO SILVA; DE SOUZA, MARIANA CARAVANTI; ROSSI, RAFAEL GERALDELI; REZENDE, SOLANGE OLIVEIRA; NOGUEIRA, BRUNO MAGALHAES; MARCACINI, RICARDO MARCONDES. One-class learning for fake news detection through multimodal variational autoencoders. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v. 122, p. 23-pg., . (19/25010-5)
DE SOUZA, MARIANA CARAVANTI; NOGUEIRA, BRUNO MAGALHAES; ROSSI, RAFAEL GERALDELI; MARCACINI, RICARDO MARCONDES; DOS SANTOS, BRUCCE NEVES; REZENDE, SOLANGE OLIVEIRA. A network-based positive and unlabeled learning approach for fake news detection. MACHINE LEARNING, . (19/25010-5)

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