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.

Yes, you can "let the model know which questions belong to which topic category." To do this, you would use what is called confirmatory factor analysis (CFA). The factor analysis wiki (link below) makes this clear.
https://en.wikipedia.org/wiki/Factor_analysis
