Development and Application of Lexicons for Identifying Political Biases in Portuguese Texts
DOI:
https://doi.org/10.5753/isys.2025.5698Keywords:
Natural Language Processing, Lexicons, Political BiasesAbstract
The Internet and social networks play a fundamental role in shaping political opinions, which makes it important to identify and analyze this type of positioning in online content. In this context, this study aims to develop two categories of lexicons to identify political biases in Portuguese texts. The first lexicon deals with economic biases (left-right) and the second with social biases (progressive-conservative). Using a corpus of 64473 speeches by deputies, we created three versions of each lexicon, one using unigrams, other using bigrams, and the last using trigrams. In the experiments conducted, the lexicons demonstrated their effectiveness in capturing biases, revealing consistent patterns for politicians' positioning on social networks and more neutral biases for news websites. Thus, the created lexicons offer a straightforward and lightweight way to analyze online discourses, potentially contributing to studies in political science and media analysis.Downloads
References
Bestvater, S. E. and Monroe, B. L. (2023). Sentiment is not stance: Target-aware opinion classification for political text analysis. Political Analysis, 31(2):235–256.
Boulianne, S. (2019). Revolution in the making? social media effects across the globe. Information, communication & society, 22(1):39–54.
Campante, F., Durante, R., and Sobbrio, F. (2018). Politics 2.0: The multifaceted effect of broadband internet on political participation. Journal of the European Economic Association, 16(4):1094–1136.
Cerqueira, M., da Silva, N. F., Souza, E., Albuquerque, H. O., Dias, M. d. S., and de Carvalho, A. C. (2025). Not only what, but also when: Understanding brazilian political comments on legislative bills over time through stance detection and topic modeling. In Conference on Digital Government Research, volume 1.
Eysenck, H. J. (1954). The psychology of politics, volume 2. Transaction publishers.
Hu, D., Jiang, S., E. Robertson, R., and Wilson, C. (2019). Auditing the partisanship of google search snippets. In The World Wide Web Conference, pages 693–704.
Jost, J. T., Federico, C. M., and Napier, J. L. (2009). Political ideology: Its structure, functions, and elective affinities. Annual review of psychology, 60(1):307–337.
Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge university press.
Mundim, P. S. (2018). O viés da cobertura política da imprensa nas eleições presidenciais brasileiras de 2002, 2006 e 2010. Revista Brasileira de Ciência Política, (25):7–46.
Pinheiro, V. and Faleiros, T. (2022). Aplicação de modelos de tópicos em análises automatizadas de discursos de senadores brasileiros. In Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional, pages 612–623, Porto Alegre, RS, Brasil. SBC.
Puglisi, R. and Snyder Jr, J. M. (2015). Empirical studies of media bias. In Handbook of media economics, volume 1, pages 647–667. Elsevier.
Resende, G., Messias, J., Silva, M., Almeida, J., Vasconcelos, M., and Benevenuto, F. (2018). A system for monitoring public political groups in whatsapp. In Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, WebMedia’18, page 387–390, New York, NY, USA. Association for Computing Machinery.
Shalders, A. (2017). Direita ou esquerda? análise de votações indica posição de partidos brasileiros no espectro ideológico. Disponível em: [link]. Acesso em: 03 jul 2024.
Shiroma, E. O., Campos, R. F., and Garcia, R. M. C. (2005). Decifrar textos para compreender a política: subsídios teórico-metodológicos para análise de documentos. Perspectiva, 23(2):427–446.
Stieglitz, S. and Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social Network Analysis and Mining, 3:1277–1291.
Superlistas, A. (2023). Os 100 brasileiros mais seguidos do twitter. Disponível em: [link]. Acesso em: 16 ago 2024.
Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2:325–347.
Vergeer, M., Hermans, L., and Sams, S. (2013). Online social networks and microblogging in political campaigning: The exploration of a new campaign tool and a new campaign style. Party Politics, 19(3):477–501.
