How to compare LDA and TF-IDF? I am doing text mining to extract topics from documents. I started with Latent Dirichlet Allocation (LDA), which worked great, but then I came across TF-IDF with K-Means clustering, which worked better for me. I'd like to evaluate both but I can'f find any useful validation or metric to compare these two. How is it possible to compare these two with a useful metric?
If you have the ground truth value of the documents (their topics) all you gotta do is pick a metric and compare results. For classification problems, as yours, a common metric would be f1_score; reference: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html