Journal
COGNITIVE COMPUTATION
Volume 15, Issue 2, Pages 440-465Publisher
SPRINGER
DOI: 10.1007/s12559-022-10048-w
Keywords
Natural language processing; Sentiment analysis; Content analysis; Aggressive discourse; Mixed methods; Twitter
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Political tensions have increased in Europe since the beginning of the new century, leading to social movements and political changes in various countries. This study examines the political discourse and underlying tensions during Madrid's elections in May 2021, using a mixed methodology approach. The findings suggest that the electoral campaign is not as negative as perceived by the citizens, and that ideologically extreme parties tend to use more aggressive language.
Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an increment of the different tensions underlying politics, as has been reported after many other political and economical crises during the twentieth century. This article proposes the study of the political discourse, and its underlying tension, during Madrid's elections (Spain) in May 2021 by using a mixed approach. To demonstrate if an aggressive tone is used during the campaign, a mixed methodology approach is applied: quantitative computational techniques, related to natural language processing, are used to conduct a first general analysis of the information screened; then, these methods are used for detecting specific trends that can be later filtered and analyzed using a qualitative approach (content analysis), which is also conducted to extract insights about the information found. The main outcomes of this study show that the electoral campaign is not as negative as perceived by the citizens and that there was no relationship between the tone of the discourse and its dissemination. The analysis confirms that the most ideologically extreme parties tend to have a more aggressive language than the moderate ones. The content analysis carried out using our methodology showed that Twitter is used as a sentiment thermometer more than as a way of communicating concrete politics.
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