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It has been widely accepted that Long Short-Term Memory (LSTM) network,coupled with attention mechanism and memory module, is useful for aspect-levelsentiment classification. However, existing approaches largely rely on themodelling of semantic relatedness of an aspect with its context words, while tosome extent ignore their syntactic dependencies within sentences. Consequently,this may lead to an undesirable result that the aspect attends on contextualwords that are descriptive of other aspects. In this paper, we propose aproximity-weighted convolution network to offer an aspect-specific syntax-awarerepresentation of contexts. In particular, two ways of determining proximityweight are explored, namely position proximity and dependency proximity. Therepresentation is primarily abstracted by a bidirectional LSTM architecture andfurther enhanced by a proximity-weighted convolution. Experiments conducted onthe SemEval 2014 benchmark demonstrate the effectiveness of our proposedapproach compared with a range of state-of-the-art models.

Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network