Large amount of categories with skewed category distribution over documents still not a closed question in the state-of-the-art technologies in automated text classification. In this paper we present a proof of concept for an automatic model of complaints screening, using text mining. Through a complaints link of the Office of the Comptroller General (CGU) site, citizens have access to a form to file their complaints. These complaints must be screened and delivered to one of 64 CGU's coordination by subject. Nowadays the complaints screening is done manually. Considering the complaints storage on the database now and the arrival of new complaints, combined with the time spent on manual sorting, the timely analysis of the reported occurrences it becomes increasingly difficult. We compare two approaches: supervised learning with classifiers algorithms and unsupervised leaning with topic modeling and text search. The best results were achieved using ranking based on the Huffman Tree algorithm. This proof of concept demonstrated the feasibility of automatic sorting without the loss of quality compared to manual sorting.
['Patricia Andrade', 'Marcelo Ladeira', 'Rommel N. Carvalho']
A Comparison Between Supervised and Unsupervised Models for Identify a Large Number of Categories