The aim of the article is to identify themes, actors and mood of the tweets shared by users in the period from March 25 to April 3, 2020 in Italy. It seems an extremely delicate and complex period, because it corresponds to the first phase of the lockdown, introduced following the Covid-19 pandemic. It was a period characterized by emergency and crisis, with nuances related to fear and uncertainty. We assumed that this situation could have influenced and produced effects on the ideologically oriented digital language practice. Taking this background into consideration, we have scraped the messages containing the surnames of the Italian Premier and the one of the opposition leader from Twitter, in order to identify the debate connected to them and to the crisis. To achieve this goal, we performed a computational linguistic technique, Emotinal Text Mining. The first result reconstructs the landscape of the debate. Arising topics-scape drawn by: the leader, the players, the economy, the entertainment, the politic, the skill, and the guilt. Then, representations were identified and sentiments measured.
The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders
La Rocca G.
2020-01-01
Abstract
The aim of the article is to identify themes, actors and mood of the tweets shared by users in the period from March 25 to April 3, 2020 in Italy. It seems an extremely delicate and complex period, because it corresponds to the first phase of the lockdown, introduced following the Covid-19 pandemic. It was a period characterized by emergency and crisis, with nuances related to fear and uncertainty. We assumed that this situation could have influenced and produced effects on the ideologically oriented digital language practice. Taking this background into consideration, we have scraped the messages containing the surnames of the Italian Premier and the one of the opposition leader from Twitter, in order to identify the debate connected to them and to the crisis. To achieve this goal, we performed a computational linguistic technique, Emotinal Text Mining. The first result reconstructs the landscape of the debate. Arising topics-scape drawn by: the leader, the players, the economy, the entertainment, the politic, the skill, and the guilt. Then, representations were identified and sentiments measured.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
