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


Abstract
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.
Anthology ID:
D19-5107
Volume:
Proceedings of the Second Workshop on Economics and Natural Language Processing
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
51–60
Language:
URL:
https://www.aclweb.org/anthology/D19-5107
DOI:
10.18653/v1/D19-5107
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5107.pdf