Latent Dirichlet Allocation and Topic Modeling

Updated by Dude Revolucion, 07/SEP/2020 to work with AWS Sagemaker Python SDK Version 2.x. Also fixed an error in generate_example_data.py that stems from floating point precision issues.

An introductory notebook on using Amazon SageMaker to train and use LDA models.

References

The example used in these notebooks come from the following paper:

• Thomas Griffiths and Mark Steyvers. Finding Scientific Topics. Proceedings of the National Academy of Science, 101(suppl 1):5228-5235, 2004.

For more details about LDA and information about the algorithm used in the Amazon SageMaker LDA algorithm consult the following papers:

• David Blei, Andrew Ng, and Michael Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(Jan):993-1022, 2003.
• Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham Kakade, and Matus Talgersy. Tensor Decompositions for Learning Latent Variable Models. Jounrla of Machine Learning Research, 15:2773-2832, 2014.
• Tamara Kolda and Brett Bader. Tensor Decompositions and Applications. SIAM REview, 51(3):455-500, 2009.