I have a dataset of text documents splitted into train and test sets. My task is a binary classification, classifying these documents to either 1 or -1. I have already computed some features using TF-IDF and n-grams and tested my model. Now, I want to add some other features using topic models (LDA and LSA) to see if it can improve the F1-score and performance of my model.
My question is fairly simply: should I create my topic models only using my train set? Or, since topic models are not created based on a label (target or dependent variable,) would it be correct if I use both train and test sets to create the topic model?