  Deep learning techniques have achieved success in aspect-based sentiment
analysis in recent years. However, there are two important issues that still
remain to be further studied, i.e., 1) how to efficiently represent the target
especially when the target contains multiple words; 2) how to utilize the
interaction between target and left/right contexts to capture the most
important words in them. In this paper, we propose an approach, called
left-center-right separated neural network with rotatory attention (LCR-Rot),
to better address the two problems. Our approach has two characteristics: 1) it
has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to
three parts of a review (left context, target phrase and right context); 2) it
has a rotatory attention mechanism which models the relation between target and
left/right contexts. The target2context attention is used to capture the most
indicative sentiment words in left/right contexts. Subsequently, the
context2target attention is used to capture the most important word in the
target. This leads to a two-side representation of the target: left-aware
target and right-aware target. We compare our approach on three benchmark
datasets with ten related methods proposed recently. The results show that our
approach significantly outperforms the state-of-the-art techniques.
