I am trying to find the common topics between articles read using the respective tags attached to each article.
Background of my mini project: The problem I am trying to solve involves looking at articles read by a group of readers who have searched the same keyword, in order to gain better understanding on the nature of content they are interested in.
As I have understood, topic models are commonly used for topic extraction. I'd like some advice on whether this would be suitable for my problem, given that I already have a dataset that contains the tags ('topics') of the articles. Or would a simple probability model be more suitable?
Illustration for simple probability model:
Keyword searched: "lifestyle"
Articles read by User 1: fashion, health, organic food, clean eating
Articles read by User 2: fitness
Articles read by User 3: recipes, diet plan, clean eating
Outcome: 25% clean eating, 12.5% diet plan etc...
Sorry, I hope my explanation isn't confusing!