  In aspect-based sentiment analysis, extracting aspect terms along with the
opinions being expressed from user-generated content is one of the most
important subtasks. Previous studies have shown that exploiting connections
between aspect and opinion terms is promising for this task. In this paper, we
propose a novel joint model that integrates recursive neural networks and
conditional random fields into a unified framework for explicit aspect and
opinion terms co-extraction. The proposed model learns high-level
discriminative features and double propagate information between aspect and
opinion terms, simultaneously. Moreover, it is flexible to incorporate
hand-crafted features into the proposed model to further boost its information
extraction performance. Experimental results on the SemEval Challenge 2014
dataset show the superiority of our proposed model over several baseline
methods as well as the winning systems of the challenge.
