Machine learning techniques in classifying satisfaction with the economy of Latin American Citizens

Abstract

Applications of machine learning algorithms offer a new perspective for exploring data from opinion polls, aiming to better understand perceptions and population profiles on various topics, including satisfaction with the economy. In this context, this research aimed to apply machine learning algorithms to classify economic satisfaction from citizens in Latin American countries, evaluating their performance on different datasets and analyzing the variables contributing most to this theme. Four base algorithms were applied: Random Forest; Gradient Boosting; XGBoost; and Naïve Bayes, along with two ensemble methods: hard voting; and soft voting. Subsequently, variable importance was assessed using Gradient Boosting and Random Forest methods, showing the contribution of the predictors to the economic satisfaction. The results showed an accuracy of approximately 86% for three of the base classifiers and soft voting but all methods performed better in classifying dissatisfied citizens, struggling to recognize patterns of those satisfied with the economy. The most important predictor variables considered for classifying this satisfaction were "Satisfaction with democracy" and "Comparison with the economy 12 months before".