Sentiment Analysis on Twitter Repercussion of Police Operations

Abstract

Violence and a sense of insecurity are among the main problems in urban centres. In Brazil, an average rate of 20 deaths per month is estimated for every 100,000 inhabitants due to violence. Virtual social networks are increasingly used as a means for users to express their opinions or indignation about this problem. In this article, we analyze the sentiment of users in comments shared on Twitter about police operations with great repercussions in news portals in Brazil. In this sense, we explore lexicon and machine learning models to understand the emotion in which users discuss public safety on social networks and their opinion about the work of government agencies to reduce violence in cities. Our experiments show how challenging this inference is given peculiar characteristics of the context, such as mostly negative and sarcastic expressions. Nevertheless, our best classifiers achieved accuracy and specificity (macro F1) greater than 60% for identifying sentiments polarity, indicating a promising methodology for automatically inferring public opinion about police operations.