Abstract
Aspect-level sentiment classification aims to predict sentiment polarities for different aspect terms within the same sentence or document. However, existing methods rely heavily on modeling the semantic relevance of an aspect term and its context words, and ignore the importance of syntax analysis to a certain extent. Consequently, this may cause the model to pay attention to the context word which is used to describe other aspect terms. In this paper, we propose a model which analyze sentences both syntactically and semantically. At the same time, we propose a simple and effective fusion mechanism to make the integration of aspect information and context information more adequately. We conduct extensive experiments on the SemEval 2014 benchmark datasets, and the results show that our model achieves a new state- of-the-art performance.
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