Expectation maximization is commonly used for topic modeling, but it often struggles with high-dimensional problems and lacks efficiency. Tensor methods, on the other hand, can be scaled effectively and parallelized, offering a promising alternative. With tools like Sagemaker and Comprehend, users can easily experiment with these advanced algorithms and access topic modeling results directly.
