In factor analysis, can we guide the model to learn the latent variables? I'm analyzing a survey result, which has 50 survey questions in Likert scale of 1-5 and the binary answer of satisfied or not as the target. I'm thinking of applying factor analysis for dimension reduction and use the factors to train a classifier to predict satisfaction. The thing is, the 50 questions are already grouped in 5 topic categories, and I'm wondering if there's any way I can let the model know which question belongs to which topic category, so that I can guide the model to learn the latent variables and thus producing more explainable factors.

As well as confirmatory factor analysis (CFA), there's an approach called Procrustes rotation to a target matrix, used in exploratory factor analysis. See, for example, https://link.springer.com/article/10.3758/s13428-019-01209-1.
Where CFA is a very strict test of the factor structure, Procrustes rotation is more relaxed. CFA says "your rotation must match this target matrix", Procrustes says "Get this rotation as close as you can to this target matrix".
