Abstract
Speeching is an intrinsic part of the work of parliamentarians, as they expose facts as well as their points of view and opinions on several subjects. This article details the analysis of relations between members of the lower house of the National Congress of Brazil during the term of office between 2011 and 2015 according to transcriptions of their house speeches. In order to accomplish this goal, Natural Language Processing and Machine Learning were used to assess pairwise relationships between members of the congress which were then observed from the perspective of Complex Networks. Node clustering was used to evaluate multiple speech-based measures of distance between each pair of political peers, as well as the resulting cohesion of their political parties. Experimental results showed that one of the proposed measures, based on aggregating similarities between each pair of speeches, is superior to a previously established alternative of considering concatenations of these elements relative to each individual when targeting to group parliamentarians organically.
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Acknowledgements
Douglas O. Cardoso acknowledges the financial support by the Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia, FCT) through grant UIDB/05567/2020, and by the European Social Fund and programs Centro 2020 and Portugal 2020 through project CENTRO-04-3559-FSE-000158.
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Lima, W.P.C., Marques, L.C., Assis, L.S., Cardoso, D.O. (2023). An Analysis of Political Parties Cohesion Based on Congressional Speeches. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14075. Springer, Cham. https://doi.org/10.1007/978-3-031-36024-4_8
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