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Sobre

Sobre

Pavel Brazdil is a founder of a strong Machine Learning / Data Mining group that exists since 1988 and which now is a part of LIAAD Inesc Tec (Laboratory of AI and Decision Support). Pavel Brazdil is Full Professor (Prof. Catedrático) at the Faculty of Economics (FEP) of University of Porto, where he has been teaching courses on Information systems, Data Mining and Text Mining. He has supervised 12 PhD students. Although he has officially retired in mid-July 2015, he continues his R&D activities, including teaching at Master and Doctoral courses and supervision of post-graduate students.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Pavel Brazdil
  • Cluster

    Informática
  • Cargo

    Investigador Afiliado
  • Desde

    01 janeiro 2010
002
Publicações

2022

On Usefulness of Outlier Elimination in Classification Tasks

Autores
Hetlerovic, D; Popelínský, L; Brazdil, P; Soares, C; Freitas, F;

Publicação
Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings

Abstract

2022

Detection of Loanwords in Angolan Portuguese: A Text Mining Approach

Autores
Muhongo, TS; Brazdil, PB; Silva, F;

Publicação
INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE

Abstract
Angola is characterized by many different languages and social, cultural and political realities, which had a marked effect on Angolan Portuguese (AP). Consequently, AP is characterized by diatopic variation. One of the marked effects is the loanwords imported from other Angolan languages. Our objective is to analyze different Angolan texts, analyze the lexical forms used and conduct a comparative study with European Portuguese, aiming at identifying the possible loanwords in Angolan Portuguese. This process was automated, as well as the identification of all loanwords' cotexts. In addition, we determine the lexical class of each loanword and the Angolan language of its origin. Most lexical loanwords come from the Kimbundu, although AP includes loanwords from some other Angolan languages too. Our study serves as a basis for preparing an Angolan regionalism dictionary. We noticed that more than 700 identified loanwords do not figure in the existing dictionaries.

2022

Semi-Automatic Approaches for Exploiting Shifter Patterns in Domain-Specific Sentiment Analysis

Autores
Brazdil, P; Muhammad, SH; Oliveira, F; Cordeiro, J; Silva, F; Silvano, P; Leal, A;

Publicação
MATHEMATICS

Abstract
This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction.

2022

Metalearning

Autores
Brazdil, P; van Rijn, JN; Soares, C; Vanschoren, J;

Publicação
Cognitive Technologies

Abstract

2022

NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis

Autores
Muhammad, SH; Adelani, DI; Ruder, S; Ahmad, IS; Abdulmumin, I; Bello, BS; Choudhury, M; Emezue, CC; Abdullahi, SS; Aremu, A; Jorge, A; Brazdil, P;

Publicação
LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION

Abstract

Teses
supervisionadas

2021

Multidimensional Time Series Analysis: A Complex Networks Approach

Autor
Vanessa Alexandra Freitas da Silva

Instituição
UP-FCUP

2021

Segmentação fonética adaptativa em voz disfónica

Autor
João Filipe Torres Costa

Instituição
UP-FEUP

2015

Análise de comentários de clientes com o auxílio a técnicas de Text Mining para determinar o nível de (in)satisfação

Autor
Ana Catarina Barbosa Forte

Instituição
UP-FEP

2015

Sentiment Analysis in Financial News

Autor
Patrícia Alexandra Lopes Antunes

Instituição
UP-FEP

2015

Development of a support system for workflow design for data mining problems that exploits Meta-learning

Autor
Salisu Mamman Abdulrahman

Instituição
UP-FEP