Abstract
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 termpolarity 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.