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Classification of Public Administration Complaints

Authors Francisco Caldeira, Luís Nunes , Ricardo Ribeiro



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Author Details

Francisco Caldeira
  • Iscte, University Institute of Lisbon, Portugal
Luís Nunes
  • Iscte, University Institute of Lisbon, Portugal
  • ISTAR, Lisbon, Portugal
Ricardo Ribeiro
  • Iscte, University Institute of Lisbon, Portugal
  • INESC-ID Lisbon, Portugal

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Francisco Caldeira, Luís Nunes, and Ricardo Ribeiro. Classification of Public Administration Complaints. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 9:1-9:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.SLATE.2022.9

Abstract

Complaint management is a problem faced by many organizations that is both vital to customer image and highly dependent on human resources. This work attempts to tackle a part of the problem, by classifying summaries of complaints using machine learning models in order to better redirect these to the appropriate responders. The main challenges of this task is that training datasets are often small and highly imbalanced. This can can have a big impact on the performance of classification models. The dataset analyzed in this work suffers from both of these problems, being relatively small and having labels in different proportions. In this work, two different techniques are analyzed: combining classes together to increase the number of elements of the new class; and, providing new artificial examples for some classes via translation into other languages. The classification models explored were the following: k-NN, SVM, Naïve Bayes, boosting, and Deep Learning approaches, including transformers. The paper concludes that although, as expected, the classes with little representation are hard to classify, the techniques explored helped to boost the performance, especially in the classes with a low number of elements. SVM and BERT-based models outperformed their peers.

Subject Classification

ACM Subject Classification
  • Information systems → Clustering and classification
Keywords
  • Text Classification
  • Natural Language Processing
  • Deep Learning
  • BERT

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References

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