Predicting satisfaction with democracy in Brazil considering opinion survey data - Rosa, Douglas Martins de Souza; dos Santos, Bruno Samways; Lima, Rafael Henrique Palma (Revista Gestão da Produção Operações e Sistemas, 2023)
Purpose – This article compared machine learning algorithms in the context of satisfaction with democracy in Brazil. The models were trained with data from the Latinobarómetro survey, a private non-profit institution. Theoretical foundation – The Support Vector Classifier (SVC), Random Forest, and Artificial Neural Networks (ANN) classification techniques were described, followed by evaluation metrics, such as accuracy, precision, recall, and f1-score. Methodology – The data set was cleaned and the questionnaire was reduced to variables related to the local democracy index (IDL). Afterward, attribute transformations were performed and subsets with different class balances were created to evaluate the performance of the classifiers in different scenarios. Also, the attributes that most contributed to satisfaction with democracy were analyzed. Results – The best classifier was Random Forest, with better results than the other applied methods, mainly for the specific class dissatisfied with democracy. Evaluating the most critical attributes for satisfaction with democracy, it was identified that they are related to the country's economic situation and political and governmental issues. Research implications – The models created were able to identify, mainly, people dissatisfied with democracy and the most important variables in this context, providing opportunities for research on the relationship between economy, government, political positioning, and democracy, while developing possible strategies to change the perception of the dissatisfied. Originality/Value – Exploring data on democracy from the Latinobarómetro using machine learning techniques in the context of Brazil.