Is it still transfer learning if you consider input as well as output? (neural networks)
This type of transfer learning only applies if you have the same features.  If you don't, you'll either have to do something else, or find some way to first extract a common set of features from both, or choose a feature set that is the union of the two features.  The latter choice would handle the example you gave of hotel reviews vs restaurant reviews.

That said, there is emerging evidence that for deep neural networks, it's not clear that transfer learning (pre-training on a different task) provides any advantage in aaccuracy over training from scratch on the second task.  Recent research has shown that if you train long enough on the second task, you get accuracy as good as if you start with weights from a network that was pre-trained on the second task.  So, it appears the main advantage of this kind of transfer learning might be to speed up training time, rather than to build better models.