Abercrombie, G., & Batista-Navarro, R. (2020). Sentiment and position-taking analysis of parliamentary debates: A systematic literature review. Journal of Computational Social Science, 3(1), 245–270.
Albalawi, R., Yeap, T. H., & Benyoucef, M. (2020). Using topic modeling methods for short-text data: A comparative analysis. Frontiers in Artificial Intelligence, 3, 42.
Allen, C., & Murdock, J. (2020). LDA topic modeling: Contexts for the history & philosophy of science.
Arun, R., Suresh, V., Veni Madhavan, C., & Murthy, N. (2010). On finding the natural number of topics with latent dirichlet allocation: Some observations. 391–402.
Bayley, P. (2004). Cross-cultural perspectives on parliamentary discourse (Vol. 10). John Benjamins Publishing.
Bergmann, H., Geese, L., Koss, C., & Schwemmer, C. (2018).
Using legislative speech to unveil conflict between coalition parties [Preprint]. SocArXiv.
https://doi.org/10.31235/osf.io/pgnwa.
Blätte, A., Gehlhar, S., & Leonhardt, C. (2020). The Europeanization of Parliamentary Debates on Migration in Austria, France, Germany, and the Netherlands. 66–74.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
Chizhik, A. V., & Sergeyev, D. A. (2021). Exploring the Parliamentary Discourse of the Russian Federation Using Topic Modeling Approach. 403–416.
Curran, B., Higham, K., Ortiz, E., & Vasques Filho, D. (2018). Look who’s talking: Two-mode networks as representations of a topic model of New Zealand parliamentary speeches.
PLOS ONE,
13(6), e0199072.
https://doi.org/10.1371/journal.pone.0199072.
de Campos, L. M., Fernandez-Luna, J. M., Huete, J. F., & Redondo-Expósito, L. (2021). LDA-based term profiles for expert finding in a political setting. Journal of Intelligent Information Systems, 56(3), 529–559.
Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., & Starič, A. (2013). Orange: Data mining toolbox in Python. The Journal of Machine Learning Research, 14(1), 2349–2353.
Erjavec, T. et al. (2021).
Linguistically annotated multilingual comparable corpora of parliamentary debates ParlaMint.ana 2.1 (v2.1) [Computer software]. Slovenian language resource repository CLARIN.SI.
http://hdl.handle.net/11356/1431Erjavec, T., & Pancur, A. (2019). Parla-CLARIN: TEI guidelines for corpora of parliamentary proceedings. Book of Abstracts of the TEI2019: What Is Text, Really.
Gkoumas, D., Pontiki, M., Papanikolaou, K., & Papageorgiou, H. (2018). Exploring the Political Agenda of the Greek Parliament Plenary Sessions (D. Fišer, M. Eskevich, & F. de Jong, Eds.).
Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.
Høyland, B., & Søyland, M. G. (2019). Electoral reform and parliamentary debates. Legislative Studies Quarterly, 44(4), 593–615.
Ilie, C. (2010). European parliaments under scrutiny: Discourse strategies and interaction practices (Vol. 38). John Benjamins Publishing.
Jacobs, T., & Tschötschel, R. (2019). Topic models meet discourse analysis: A quantitative tool for a qualitative approach. International Journal of Social Research Methodology, 22(5), 469–485.
Jones, K. S. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation.
Kilroy, D. (2021). All the king’s men? A demographic study of opinion in the first English Parliament of James I, 1604–10. Parliaments, Estates and Representation, 41(1), 1–23.
Martin, F., & Johnson, M. (2015). More efficient topic modelling through a noun only approach. 111–115.
Meeks, E., & Weingart, S. B. (2012). The digital humanities contribution to topic modeling. Journal of Digital Humanities, 2(1), 1–6.
Mollin, S. (2007). The Hansard hazard: Gauging the accuracy of British parliamentary transcripts. Corpora, 2(2), 187–210.
Morstatter, F., Shao, Y., Galstyan, A., & Karunasekera, S. (2018). From alt-right to alt-rechts: Twitter analysis of the 2017 German federal election. 621–628.
Müller-Hansen, F., Callaghan, M. W., Lee, Y. T., Leipprand, A., Flachsland, C., & Minx, J. C. (2021). Who cares about coal? Analyzing 70 years of German parliamentary debates on coal with dynamic topic modeling. Energy Research & Social Science, 72, 101869.
Norton, P. (2002). Parliaments and citizens in Western Europe (Vol. 3). Psychology Press.
Pančur, A., & Šorn, M. (2016). Digitalni pristop k parlamentarni zgodovini: Uporaba gradiva Državnega zbora v digitalni humanistiki. Četrt stoletja Republike Slovenije - izzivi, dileme, pričakovanja, 115–126.
Petukhova, V., Malchanau, A., & Bunt, H. (2015). Modelling argumentation in parliamentary debates (M. Baldoni & et al., Eds.). Springer.
Piersma, H., Tames, I., Buitinck, L., Van Doornik, J., & Marx, M. (2014). War in parliament: What a digital approach can add to the study of parliamentary history. Digital Humanities Quarterly, 8(1).
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155(2), 945–959.
Proksch, S.-O., & Slapin, J. B. (2010). Position taking in European Parliament speeches. British Journal of Political Science, 40(3), 587–611.
Rheault, L., Beelen, K., Cochrane, C., & Hirst, G. (2016). Measuring emotion in parliamentary debates with automated textual analysis. PloS One, 11(12), e0168843.
Rheault, L., & Cochrane, C. (2020). Word embeddings for the analysis of ideological placement in parliamentary corpora. Political Analysis, 28(1), 112–133.
Rosa, A. B., Gudowsky, N., & Repo, P. (2021). Sensemaking and lens-shaping: Identifying citizen contributions to foresight through comparative topic modelling. Futures, 129, 102733.
Rudkowsky, E., Haselmayer, M., Wastian, M., Jenny, M., Emrich, Š., & Sedlmair, M. (n.d.). Supervised Sentiment Analysis of Parliamentary Speeches and News Reports.
Schmidt, B. M. (2012). Words alone: Dismantling topic models in the humanities. Journal of Digital Humanities, 2(1), 49–65.
Schuler, P. (2020). Position taking or position ducking? A theory of public debate in single-party legislatures. Comparative Political Studies, 53(9), 1493–1524.
Serrano, J. C. M., Shahrezaye, M., Papakyriakopoulos, O., & Hegelich, S. (2019). The rise of Germany’s AfD: A social media analysis. 214–223.
Shadrova, A. (2021). Topic models do not model topics: Epistemological remarks and steps towards best practices. Journal of Data Mining & Digital Humanities, 2021.
Sieberer, U., Müller, W. C., & Heller, M. I. (2011). Reforming the rules of the parliamentary game: Measuring and explaining changes in parliamentary rules in Austria, Germany, and Switzerland, 1945–2010. West European Politics, 34(5), 948–975.
Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. 63–70.
Smith, N., & Graham, T. (2019). Mapping the anti-vaccination movement on Facebook. Information, Communication & Society, 22(9), 1310–1327.
Truan, N., & Romary, L. (2021). Building, Encoding, and Annotating a Corpus of Parliamentary Debates in XML-TEI: A Cross-Linguistic Account. Journal of the Text Encoding Initiative.
van der Zwaan, J. M., Marx, M., & Kamps, J. (2016). Validating Cross-Perspective Topic Modeling for Extracting Political Parties’ Positions from Parliamentary Proceedings. 28–36.
Vayansky, I., & Kumar, S. A. (2020). A review of topic modeling methods. Information Systems, 94, 101582.
Wiedemann, G. (2016). Text mining for qualitative data analysis in the social sciences (Vol. 1). Springer.
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. 16(13), 1–10.