@inproceedings{filgueiras-etal-2019-complaint,
title = "Complaint Analysis and Classification for Economic and Food Safety",
author = "Filgueiras, Jo{\~a}o and
Barbosa, Lu{\'\i}s and
Rocha, Gil and
Lopes Cardoso, Henrique and
Reis, Lu{\'\i}s Paulo and
Machado, Jo{\~a}o Pedro and
Oliveira, Ana Maria",
editor = "Hahn, Udo and
Hoste, V{\'e}ronique and
Zhang, Zhu",
booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5107",
doi = "10.18653/v1/D19-5107",
pages = "51--60",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Complaint Analysis and Classification for Economic and Food Safety
%A Filgueiras, João
%A Barbosa, Luís
%A Rocha, Gil
%A Lopes Cardoso, Henrique
%A Reis, Luís Paulo
%A Machado, João Pedro
%A Oliveira, Ana Maria
%Y Hahn, Udo
%Y Hoste, Véronique
%Y Zhang, Zhu
%S Proceedings of the Second Workshop on Economics and Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F filgueiras-etal-2019-complaint
%X 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.
%R 10.18653/v1/D19-5107
%U https://aclanthology.org/D19-5107
%U https://doi.org/10.18653/v1/D19-5107
%P 51-60
Markdown (Informal)
[Complaint Analysis and Classification for Economic and Food Safety](https://aclanthology.org/D19-5107) (Filgueiras et al., 2019)
ACL
- João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, and Ana Maria Oliveira. 2019. Complaint Analysis and Classification for Economic and Food Safety. In Proceedings of the Second Workshop on Economics and Natural Language Processing, pages 51–60, Hong Kong. Association for Computational Linguistics.