The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders

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....

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Main Authors: Francesca Greco, Gevisa La Rocca
Format: Article
Language:English
Published: University of Salerno 2020-06-01
Series:Culture e Studi del Sociale
Subjects:
Online Access:http://www.cussoc.it/index.php/journal/article/view/134
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spelling doaj-e2efefa9ab7447bbb0f1d7f51832fdb82020-12-21T14:58:10ZengUniversity of SalernoCulture e Studi del Sociale2531-39752020-06-0151 Special335346The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political LeadersFrancesca Greco0Gevisa La Rocca1Sapienza University of Rome, ItalyKore University of Enna, ItalyThe 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.http://www.cussoc.it/index.php/journal/article/view/134emotional text miningpolitical debatetwitter
collection DOAJ
language English
format Article
sources DOAJ
author Francesca Greco
Gevisa La Rocca
spellingShingle Francesca Greco
Gevisa La Rocca
The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders
Culture e Studi del Sociale
emotional text mining
political debate
twitter
author_facet Francesca Greco
Gevisa La Rocca
author_sort Francesca Greco
title The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders
title_short The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders
title_full The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders
title_fullStr The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders
title_full_unstemmed The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders
title_sort topics-scape of the pandemic crisis: the italian sentiment on political leaders
publisher University of Salerno
series Culture e Studi del Sociale
issn 2531-3975
publishDate 2020-06-01
description 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.
topic emotional text mining
political debate
twitter
url http://www.cussoc.it/index.php/journal/article/view/134
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