Abstract
AbstractWe contribute to the growing number of studies on emotions and politics by investigating how political parties strategically use sentiments and emotions in party manifestos. We use computational methods in examining changes of sentiments and emotions in Finnish party manifestos from 1945 to 2019. We use sentiment and emotion lexicons first translated from English into Finnish and then modified for the purposes of our study. We analyze how the use of emotions and sentiments differs between government and opposition parties depending on their left/right ideology and the specific type of party manifesto. In addition to traditional sentiment and emotion analysis, we use emotion intensity analysis. Our results indicate that in Finland, government and opposition parties do not differ substantially from each other in their use of emotional language. From a historical perspective, the individual emotions used in party manifestos have persisted, but changes have taken place in the intensity of using emotion words. We also find that in comparison with other parties, populist parties both appeal to different emotions and appeal to the same emotions with different intensities.
Funder
University of Helsinki including Helsinki University Central Hospital
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Transportation
Reference50 articles.
1. Ahonen, P., & Koljonen, J. (2018). Transitory and Resilient Salient Issues in Party Manifestos, Finland, 1880s to 2010s. In 10th International Conference, SocInfo 2018, St. Petersburg, Russia, September 25-28, 2018, Proceedings, Part I. https://doi.org/10.1007/978-3-030-01129-1_2
2. Alm, C. O., Roth, D., & Sproat, R. (2005). Emotions from text: Machine learning for text-based emotion prediction. In Proceedings of human language technology conference and conference on empirical methods in natural language processing (pp. 579–586).
3. Araque, O., Gatti, L., Staiano, J., & Guerini, M. (2019). Depechemood++: A bilingual emotion lexicon built through simple yet powerful techniques. IEEE transactions on affective computing.
4. Benoit, K., & Laver, M. (2006). Party policy in modern democracies. Routledge.
5. Bielik, I. (2020). Application of natural language processing to the electoral manifestos of social democratic parties in central eastern european countries. Politics in Central Europe, 16, 259–282.