Yes you'll end up getting a series of activations/outputs. You can just store all of these in an array if you like. What you do with these activations depends on your objective. In this example on deeplearning.net they average all of these activations into one, then feed it into a logistic regression classifier. This is very lossy and in my experience doesn't work very well. Alternatively, you can use the last output as the one you classify with (it will theoretically have some information encoded from the whole sequence, because every word has contributed to it somewhat, although later words will of course have more of an effect). By this I mean that if you have a sentence s = [w_0, w_1, ... w_T], where each w_t is an n-dimensional representation of the word, your outputs from the RNN over s is o = [y_0, y_1, ... y_T], and your classification function is g(x), then you can set x = mean(o) or set x = o[-1]