Aspect-level sentiment classification aims to identify the polarity of a target word in a sentence. Studies on sentiment classification have found that a target's surrounding words have great impacts and global attention to the target. However, existing neural-network-based models either depend on expensive phrase-level annotation or do not fully exploit the association of the context words to the target. In this paper, we propose to model the influences of the target's surrounding words via two unidirectional long short-term memory neural networks, and introduce a target-based attention mechanism to discover the underlying relationship between the target and the context words. Empirical results on the SemEval 2014 Datasets show that our approach outperforms many competitive sentiment classification baseline methods. Detailed analysis demonstrates the effectiveness of the proposed surrounding-based long-short memory neural networks and the target-based attention mechanism.
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