  Sentiment analysis has transitioned from classifying the sentiment of an
entire sentence to providing the contextual information of what targets exist
in a sentence, what sentiment the individual targets have, and what the causal
words responsible for that sentiment are. However, this has led to elaborate
requirements being placed on the datasets needed to train neural networks on
the joint triplet task of determining an entity, its sentiment, and the causal
words for that sentiment. Requiring this kind of data for training systems is
problematic, as they suffer from stacking subjective annotations and domain
over-fitting leading to poor model generalisation when applied in new contexts.
These problems are also likely to be compounded as we attempt to jointly
determine additional contextual elements in the future. To mitigate these
problems, we present a hybrid neural-symbolic method utilising a Dependency
Tree-LSTM's compositional sentiment parse structure and complementary symbolic
rules to correctly extract target-sentiment-cause triplets from sentences
without the need for triplet training data. We show that this method has the
potential to perform in line with state-of-the-art approaches while also
simplifying the data required and providing a degree of interpretability
through the Tree-LSTM.
