  Sentiment analysis can be regarded as a relation extraction problem in which
the sentiment of some opinion holder towards a certain aspect of a product,
theme or event needs to be extracted. We present a novel neural architecture
for sentiment analysis as a relation extraction problem that addresses this
problem by dividing it into three subtasks: i) identification of aspect and
opinion terms, ii) labeling of opinion terms with a sentiment, and iii)
extraction of relations between opinion terms and aspect terms. For each
subtask, we propose a neural network based component and combine all of them
into a complete system for relational sentiment analysis. The component for
aspect and opinion term extraction is a hybrid architecture consisting of a
recurrent neural network stacked on top of a convolutional neural network. This
approach outperforms a standard convolutional deep neural architecture as well
as a recurrent network architecture and performs competitively compared to
other methods on two datasets of annotated customer reviews. To extract
sentiments for individual opinion terms, we propose a recurrent architecture in
combination with word distance features and achieve promising results,
outperforming a majority baseline by 18% accuracy and providing the first
results for the USAGE dataset. Our relation extraction component outperforms
the current state-of-the-art in aspect-opinion relation extraction by 15%
F-Measure.
