Analyzing the Emotions That News Agencies Express towards Candidates during Electoral Campaigns: 2018 Brazilian Presidential Election as a Case of Study

Since online social networks play an increasingly important role in the final voting decision of each individual, political parties and candidates are changing the way of doing politics and campaigning, increasing their digital presence. In this paper, we propose a methodology to analyze and measure the emotions that news agencies express on social media towards candidates and apply it to the 2018 Brazilian elections. The presented method is based on a sentiment analysis and emotion mining by means of machine learning and Natural Language Processing approaches such as Naive Bayes classification and Stemming calculation. We found that if doing basic sentiment detection, nearly all posts are neutral. However, when we analyze emotions, following Ekman's six basic emotions, we do not find neutrality but clear and identifiable emotions. Next, we present and discuss the associative patterns between news agencies and presidential candidates. Finally, since the candidate that captured the highest and most negative attention emerged victorious in the elections, we discuss the potential importance of having a social media presence, regardless of generating positive or negative emotions.