  Many high performance machine learning models for Aspect-Based Sentiment
Classification (ABSC) produce black box models, and therefore barely explain
how they classify a certain sentiment value towards an aspect. In this paper,
we propose explanation models, that inspect the internal dynamics of a
state-of-the-art neural attention model, the LCR-Rot-hop, by using a technique
called Diagnostic Classification. Our diagnostic classifier is a simple neural
network, which evaluates whether the internal layers of the LCR-Rot-hop model
encode useful word information for classification, i.e., the part of speech,
the sentiment value, the presence of aspect relation, and the aspect-related
sentiment value of words. We conclude that the lower layers in the LCR-Rot-hop
model encode the part of speech and the sentiment value, whereas the higher
layers represent the presence of a relation with the aspect and the
aspect-related sentiment value of words.
