  Explaining the outcome of deep learning decisions based on affect is
challenging but necessary if we expect social companion robots to interact with
users on an emotional level. In this paper, we present a commonsense approach
that utilizes an interpretable hybrid neural-symbolic system to associate
extracted targets, noun chunks determined to be associated with the expressed
emotion, with affective labels from a natural language expression. We leverage
a pre-trained neural network that is well adapted to tree and sub-tree
processing, the Dependency Tree-LSTM, to learn the affect labels of dynamic
targets, determined through symbolic rules, in natural language. We find that
making use of the unique properties of the recursive network provides higher
accuracy and interpretability when compared to other unstructured and
sequential methods for determining target-affect associations in an
aspect-based sentiment analysis task.
