João Filgueiras


2019

pdf bib
Complaint Analysis and Classification for Economic and Food Safety
João Filgueiras | Luís Barbosa | Gil Rocha | Henrique Lopes Cardoso | Luís Paulo Reis | João Pedro Machado | Ana Maria Oliveira
Proceedings of the Second Workshop on Economics and Natural Language Processing

Governmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and machine learning techniques are being used to process citizen feedback. In this paper, we report on the use of such techniques for analyzing and classifying complaints, in the context of the Portuguese Economic and Food Safety Authority. Grounded in its operational process, we address three different classification problems: target economic activity, implied infraction severity level, and institutional competence. We show promising results obtained using feature-based approaches and traditional classifiers, with accuracy scores above 70%, and analyze the shortcomings of our current results and avenues for further improvement, taking into account the intended use of our classifiers in helping human officers to cope with thousands of yearly complaints.

2014

pdf bib
TUGAS: Exploiting unlabelled data for Twitter sentiment analysis
Silvio Amir | Miguel B. Almeida | Bruno Martins | João Filgueiras | Mário J. Silva
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

pdf bib
REACTION: A naive machine learning approach for sentiment classification
Silvio Moreira | João Filgueiras | Bruno Martins | Francisco Couto | Mário J. Silva
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)