I am undertaking text analysis of some twitter data. In the end I want to have a data that is interpretable. And so in the end I would like to reduce the data to relevant unit of analysis. Topic models seem a nice fit since they reduce the noise significantly, but still at the end there are quite a lot of trash topic lists which do not mean anything.What would be the best way to remove these topics. Is there a working example of this?
I am using LDA. Probably I will use Gensim. Its going to be unsupervised and its a simple twitter data. So the whole corpora will be 1500 tweets of 160 characters.