Emotion recognition in Italian political language to predict positionings and crises government.

The paper aims to analyze the political language adopted on Twitter by the main Italian parties’ leaders during the first two waves of Covid-19 pandemic. A two-step model based on sentiment emotion recognition (ER) and Correspondence analysis detected which emotions characterized the political language and which changes happened between the two waves. The results showed the use of a language with a strong emotional weight for some political actors as opposed to others who used a neutral register of political language in both waves. The comparison between two waves denoted a shift from anger to sadness and fear for Meloni and a moving away Salvini by predicting through ER the rift of the right-wing.