  This paper focuses on two related subtasks of aspect-based sentiment
analysis, namely aspect term extraction and aspect sentiment classification,
which we call aspect term-polarity co-extraction. The former task is to extract
aspects of a product or service from an opinion document, and the latter is to
identify the polarity expressed in the document about these extracted aspects.
Most existing algorithms address them as two separate tasks and solve them one
by one, or only perform one task, which can be complicated for real
applications. In this paper, we treat these two tasks as two sequence labeling
problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate
all aspect term-polarity pairs of the input sentence simultaneously.
Specifically, DOER involves a dual recurrent neural network to extract the
respective representation of each task, and a cross-shared unit to consider the
relationship between them. Experimental results demonstrate that the proposed
framework outperforms state-of-the-art baselines on three benchmark datasets.
