Session 9

🔨 Advanced Methods: Topic Modeling

Participate

🖥️ Session 09

Suggested readings

• Chen, Y., Peng, Z., Kim, S.-H., & Choi, C. W. (2023). What We Can Do and Cannot Do with Topic Modeling: A Systematic Review. Communication Methods and Measures, 17(2), 111–130. https://doi.org/10.1080/19312458.2023.2167965

• Hase, V. (2023). Automated Content Analysis (F. Oehmer-Pedrazzi, S. H. Kessler, E. Humprecht, K. Sommer, & L. Castro, Eds.; pp. 23–36). Springer Fachmedien Wiesbaden. https://link.springer.com/10.1007/978-3-658-36179-2_3

• Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures, 12(2-3), 93–118. https://doi.org/10.1080/19312458.2018.1430754

Additional readings

• Bernhard, J., Teuffenbach, M., & Boomgaarden, H. G. (2023). Topic Model Validation Methods and their Impact on Model Selection and Evaluation. Computational Communication Research, 5(1), 1. https://doi.org/10.5117/CCR2023.1.13.BERN

• Churchill, R., & Singh, L. (2022). The Evolution of Topic Modeling. ACM Computing Surveys, 54(10s), 1–35. https://doi.org/10.1145/3507900

• Denny, M. J., & Spirling, A. (2018). Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It. Political Analysis, 26(2), 168–189. https://doi.org/10/gdjsqk

• Maier, D., Niekler, A., Wiedemann, G., & Stoltenberg, D. (2020). How Document Sampling and Vocabulary Pruning Affect the Results of Topic Models. Computational Communication Research, 2(2), 139–152. https://doi.org/10.5117/CCR2020.2.001.MAIE

• Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). How to Analyze Political Attention with Minimal Assumptions and Costs. American Journal of Political Science, 54(1), 209–228. https://doi.org/10.1111/j.1540-5907.2009.00427.x

• Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural Topic Models for Open-Ended Survey Responses. American Journal of Political Science, 58(4), 1064–1082. https://doi.org/10.1111/ajps.12103

Useful packages

• quanteda 🌐 |
  Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). Quanteda: An r package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. https://doi.org/10.21105/joss.00774

• stm 🌐 | für Structural Topic Modeling
  Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). stm: An R Package for Structural Topic Models. Journal of Statistical Software, 91(1), 1–40. https://doi.org/10.18637/jss.v091.i02

• keyATM 🌐 | für Keyword Assisted Topic Modeling
  Esser, F. (2019). Comparative international studies of election campaign communication: What should happen next? Journalism, 20(8), 1124–1138. https://doi.org/10.1177/1464884919845450

• topicmodels 🌐 für LDA basiertes Verfahren
  Grün, B., & Hornik, K. (n.d.). topicmodels: Topic Models. https://doi.org/10.32614/CRAN.package.topicmodels

• tidytext 🌐 | für Extraktion z.B. der Theta- oder Phi-Matrix
  Silge, J., & Robinson, D. (2016). Tidytext: Text mining and analysis using tidy data principles in r. The Journal of Open Source Software, 1(3), 37. https://doi.org/10.21105/joss.00037

• LDAvis zur Visualisierung
  Sievert, C., & Shirley, K. (n.d.). LDAvis: Interactive Visualization of Topic Models. https://doi.org/10.32614/CRAN.package.LDAvis

• stminsights 🌐 | zur Visualisierung
  Schwemmer, C. (2021). Stminsights: A shiny application for inspecting structural topic models. https://github.com/cschwem2er/stminsights

• oolong 🌐 | für Validierungen
  Chan, C., & Sältzer, M. (2020). Oolong: An r package for validating automated content analysis tools. Journal of Open Source Software, 5(55), 2461. https://doi.org/10.21105/joss.02461

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