Understanding LSTM dimension data reported by Tensorflow's get_variable_to_shape_map()

I am attempting to use a Tensorflow LSTM to do sentiment analysis of tweet data. The program runs fine, but my grasp of what the algorithm is doing is so weak that I'm not sure I can trust the results. I'm hoping someone can clarify for me some of the dimension numbers that get_variable_to_shape_map() reports for the LSTM, because they are not what I expected. The 2 is the dimension of the label, and the 512 is the size of the batch and 128 is the number of hidden layers, but I do not know where the 228 comes from, and for that matter, I don't really understand why the 128 and the 512 show up where they do. Neither of the variables have dimensions matching the placeholders.