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Deep learning techniques have achieved success in aspect-based sentimentanalysis in recent years. However, there are two important issues that stillremain to be further studied, i.e., 1) how to efficiently represent the targetespecially when the target contains multiple words; 2) how to utilize theinteraction between target and left/right contexts to capture the mostimportant words in them. In this paper, we propose an approach, calledleft-center-right separated neural network with rotatory attention (LCR-Rot),to better address the two problems. Our approach has two characteristics: 1) ithas three separated LSTMs, i.e., left, center and right LSTMs, corresponding tothree parts of a review (left context, target phrase and right context); 2) ithas a rotatory attention mechanism which models the relation between target andleft/right contexts. The target2context attention is used to capture the mostindicative sentiment words in left/right contexts. Subsequently, thecontext2target attention is used to capture the most important word in thetarget. This leads to a two-side representation of the target: left-awaretarget and right-aware target. We compare our approach on three benchmarkdatasets with ten related methods proposed recently. The results show that ourapproach significantly outperforms the state-of-the-art techniques.

Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention