21  Topic Modeling

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Topic Modeling

LDA (Blei et al., 2003)

Dirichlet is generallly pronounced either “Deereekleh” or “Deerishleh”

Poldrack et al. (2012)

library(seededlda)

lda <- textmodel_lda(dfm, k = 10, verbose = TRUE)

For larger corpora, set batch_size lower

https://psycnet.apa.org/record/2021-27454-001

21.1 Supervised LDA

Blei & McAuliffe (2010)

sLDA in R

21.2 Semi-Supervised LDA

seededLDA in R

An Example of Semi-Supervised LDA in Research: Curini & Vignoli (2021)

21.3 BERTopic: Neural Topic Modeling

Grootendorst (2022)

devtools::install_github("abresler/bertopic")

Advantages of Topic Modeling

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Disadvantages of Topic Modeling

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Blei, D. M., & McAuliffe, J. D. (2010). Supervised topic models. https://arxiv.org/abs/1003.0783

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res., 3(null), 993–1022. https://doi.org/10.5555/944919.944937

Curini, L., & Vignoli, V. (2021). Committed Moderates and Uncommitted Extremists: Ideological Leaning and Parties’ Narratives on Military Interventions in Italy. Foreign Policy Analysis, 17(3), orab016. https://doi.org/10.1093/fpa/orab016

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv Preprint arXiv:2203.05794.

Poldrack, R. A., Mumford, J. A., Schonberg, T., Kalar, D., Barman, B., & Yarkoni, T. (2012). Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping. PLOS Computational Biology, 8(10), e1002707. https://doi.org/10.1371/journal.pcbi.1002707