Seeded topic modeling as a more appropriate alternative to unsupervised standard topic models

Arora S Ge R Moitra A (2012) Learning topic models—going beyond SVD. In: IEEE 53rd annual symposium on foundations of computer science, New Brunswick, NJ, 20–23 October 2012, pp.1–10. IEEE.

Bearman P (2015) Big data and historical social science. Big Data & Society 2(2): 2053951715612497.

Blei DM Ng AY Jordan MI (2003) Latent Dirichlet allocation. Journal of Machine Learning Research 3: 993–1022.

Bodell MH Magnusson M Keuschnigg M (2022) Seeded topic models in digital archives: Analyzing the Swedish understanding of immigration, 1945–2019. OSF Preprints. Available at: https://osf.io/preprints/osf/5uq26 (accessed 28 July 2024).

Fritz C (2007) From Early English in Australia to Australian English 1788–1900. Frankfurt: Peter Lang.

Jagarlamudi J Daumé H III Udupa R (2012) Incorporating lexical priors into topic models. In: 13th conference of the European chapter of the association for computational linguistics, Avignon, France, 23–27 April 2012, pp.204–213. Association for Computational Linguistic.

Magnusson M Jonsson L Villani M , et al (2018) Sparse partially collapsed MCMC for parallel inference in topic models. Journal of Computational and Graphical Statistics 27(2): 449–463.

Rule A Cointet JP Bearman PS (2015) Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790–2014. Proceedings of the National Academy of Sciences 112(35): 10837–10844.

Schweinberger M (2024a) Topic Modelling With R. Brisbane, QLD: The University of Queensland. Available at: https://ladal.edu.au/topicmodels.html (accessed 28 July 2024).

Schweinberger M (2024b) A computational approach to analysing the Corpus of Oz Early English. In: Amador-Moreno C Haumann D Peters A (eds) The Words That Remain: (Doing) Historical Linguistics in the Digital Era. London: Routledge, pp.73–88.

Schweinberger M (2024c) Topic Modelling Tool. Brisbane, QLD: The University of Queensland. Available at: https://ladal.edu.au/tools.html (accessed 28 July 2024).

Watanabe K Baturo A (2024) Seeded sequential LDA: A semi-supervised algorithm for topic-specific analysis of sentences. Social Science Computer Review 42(1): 224–248.

Watanabe K Xuan-Hieu P (2024) Seededlda: Seeded sequential LDA for topic modeling. R package version 1.2.1. Available at: https://CRAN.R-project.org/package=seededlda (accessed 28 July 2024).