I decided to try feeding the ELMo vectors from my second example into the LSTM classifer from the first, and the results are rather underwhelming. It gives the same answers for any input for every mood except possibly “determined” or “aware” (one of them changes a little bit each time, not sure which).
So, something is not quite right in there. One thing I did was to take out the “embedding”, since it needed a “vocabulary size” and there is really no vocabulary size in an array of floating point numbers. It’s also possible that LSTM is just not the right kind of network for this problem, or that any one of the parameters is not quite right… I will have to do some research.