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Emotion recognition in italian political language for prefiguring crisis in the balance of the parties’ coalitions

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Gepubliceerd in:Quality and Quantity vol. 58, no. 2 (Apr 2024), p. 1971
Hoofdauteur: Forciniti, Alessia
Andere auteurs: Zavarrone, Emma, Paolillo, Mirella
Gepubliceerd in:
Springer Nature B.V.
Onderwerpen:
Politics
Data processing
Political discourse
Correspondence analysis
COVID-19
Data analysis
Emotion recognition
Recognition
Language
Emotions
Happiness
Acknowledgment
Fear & phobias
Political parties
Political leadership
Vocabulary
Italian language
Discourse
Sentiment analysis
Grief
Political communication
Right wing politics
Coalitions
Anger
Natural language processing
Sadness
Centrism
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Samenvatting:The purpose of this study is to describe the emotions that dominated the political vocabulary used on Twitter during the Covid-19 epidemic by the leaders of the major Italian political parties, including Forza Italia, Movimento 5 Stelle, Fratelli d’Italia, Italia Viva, Lega, and Partito Democratico. We developed a 4-step analysis model based on Natural Language Processing (NLP) that combines: (1) Exploratory Textual Data Analysis; (2) Emotion Recognition (ER) for Italian language; (3) cross-tabulation coding between leaders and emotions; (4) the multivariate approach of Correspondence Analysis in order to determine the associations between politicians and four categories of emotions: anger, fear, sadness, and joy. Specifically, we contrasted the language utilized by Covid-19 leaders throughout the first and second waves. The results reveal an intriguing shift in the emotions communicated through political discourse between the first and second waves. Giorgia Meloni’s position as head of Fratelli d’Italia shifted significantly from fury to fear and grief, aligning her more with centrists and foreshadowing a serious rift in the right-wing and the balance of coalitions in the political arena. Giorgia Meloni became Italy’s Prime Minister in September 2022. This political success shows that the sentiment analysis done in this paper could be a good way to predict the future.
ISSN:0033-5177
1573-7845
DOI:10.1007/s11135-023-01729-1
Bron:ABI/INFORM Global