Extracting popular keyword terms or topics from blog posts based on post usage I have dataset of posts from blog and for each post I have the number of views.  I want to extract the topics (or phrases) that made the posts with more views. 

I am planning divide all posts in two sets based on number of views (one set with low number of views and the other with higher numbers), then extract topics using LDA from each set and compare how they differ.

I am wondering if this is right approach and if there are other approaches that can be better or similar?
Seems right. However, establishing causality will not be as simple as extracting keywords and noticing the differences. And I would suggest not to divide the posts, instead club them together run LDA, extract the keywords, then analyse the differences. By separating you are introducing quite a huge bias into your model.