Automatic Identification of Economic Activities in Complaints

In recent years, public institutions have undergone a progressive modernization process, bringing several administrative services to be provided electronically. Some institutions are responsible for analyzing citizen complaints, which come in huge numbers and are mainly provided in free-form text, demanding for some automatic way to process them, at least to some extent. In this work, we focus on the task of automatically identifying economic activities in complaints submitted to the Portuguese Economic and Food Safety Authority (ASAE), employing natural language processing (NLP) and machine learning (ML) techniques for Portuguese, which is a language with few resources. We formulate the task as several multi-class classification problems, taking into account the economic activity taxonomy used by ASAE. We employ features at the lexical, syntactic and semantic level using different ML algorithms. We report the results obtained to address this task and present a detailed analysis of the features that impact the performance of the system. Our best setting obtains an accuracy of 0.8164 using SVM. When looking at the three most probable classes according to the classifier’s prediction, we report an accuracy of 0.9474.