R package topicmodels: Topic models. Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Aggarwal, Charu C.: Machine learning for text (2018)
- Bouveyron, C.; Latouche, P.; Zreik, R.: The stochastic topic block model for the clustering of vertices in networks with textual edges (2018)
- Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
- Mair, Patrick: Modern psychometrics with R (2018)
- Mankad, Shawn; Hu, Shengli; Gopal, Anandasivam: Single stage prediction with embedded topic modeling of online reviews for mobile app management (2018)
- Taylor B. Arnold: A Tidy Data Model for Natural Language Processing using cleanNLP (2017) arXiv
- Rusch, Thomas; Hofmarcher, Paul; Hatzinger, Reinhold; Hornik, Kurt: Model trees with topic model preprocessing: an approach for data journalism illustrated with the WikiLeaks Afghanistan war logs (2013)
- Zhao, Yanchang: R and data mining. Examples and case studies (2013)
- Bettina Grün; Kurt Hornik: topicmodels: An R Package for Fitting Topic Models (2011) not zbMATH