How much do Twitter posts affect voters? Analysis of the multi-emotional charge with affective computing in political campaigns

Abstract

Artificial intelligence has been applied to different sectors in the government from data analysis to predictive analytics, as well as, policing, combating the COVID-19 pandemic, and political election campaigns. In the academic field, the exploitation and understanding of data generated in social media have mostly focused on unimodal sentiment analysis, based on one-dimensional sentiment analysis. This research proposes an analysis of the emotional charge for the U.S. presidential elections in 2020, based on a hybrid approach that combines affective computing and classic statistical analysis. We analyze the multi-emotional charge of candidates and voters, as well as the potential relationship of the candidates' emotions on the voters. Through this analysis is possible to determine the degree of agreement between candidates and voters. Future research is proposed for the area of affective computing in political campaigns.