[zackchase]

Hi Ian,

First off, thanks for your interest in our work! The tech is a homegrown recurrent neural network (deepx) which is available via pip. https://pypi.python.org/pypi/deepx/ We use theano for compilation to GPU and an original neural network architecture (a concatenated input generative model) to preserve the signal of auxiliary inputs (like star rating and beer category) across long intervals.

To run the model in reverse, we infer the probability of a category via the likelihood of a review. Because the prior over the categories is uniform (balanced dataset) and because the normalization term (marginal probability of a review) doesn't depend on the category, we can exploit the fact that the probability of a beer category given a review is proportional to the probability of a review given a category. In this way we're able to make a text classifier that takes into account word order.

Regarding Google Analytics, we just noticed this ourselves and are fixing it presently. Concerning color, that's a great point and I apologize for the oversight. We certainly bear no malice towards those with protanopia. In future work (and if we have subsequent versions of this paper) we'll do our best to make the lines dotted, dashed, etc to make them more readable to those unable to distinguish reg-green. Generally, I agree with you that a black and white printing should retain all readability.

Thanks for your interest and helpful observations.

Cheers,

Zack