Aspect-level sentiment classification aims to identify the sentiment polarity of a specific aspect in a sentence. In recent years, many researchers have sought to explore aspect-specific representation via attention mechanisms. Although a remarkable improvement in aspect-level sentiment classification has been achieved, these methods still suffer from deteriorative performance in certain cases for multiple reasons. First, they adopt a coarse-grained attention mechanism, which may cause information loss when the contextual sentence is long or includes multiple sentiments, and the aspect contains multiple words. Second, they consider only the influence of contextual keywords on sentiment polarity, ignoring the importance of key phrases in a sentence. To address these issues, a phrase-aware neural network based on fine-grained attention,referred to as FAPN, is proposed. The FAPN employs a convolutional neural network to extract phrase representations in the context and concatenates the representations with the corresponding word vector as input. Additionally, a fine-grained attention module is designed to generate aspect-specific representations by capturing the word-level interactions between the aspect and the sentence. Extensive experiments on five widely used benchmark datasets demonstrate the effectiveness of the proposed FAPN method.
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