Structural Position Network for Aspect-Based Sentiment Classification

Abstract

Aspect-based sentiment classification aims to discriminate the polarity of each aspect term for a given sentence. Previous works mainly focus on sequential modeling and aspect representations. However, the syntactical information and relative structural position of aspect in sentence are neglected, resulting in some irrelevant contextual words as clues during the identification process of aspect sentiment. This paper proposes a structural position network (SPNet) based on bidirectional long short-term memory (LSTM) for further integrating syntactical information. Specifically, we first utilize the dependency tree to represent the grammatical structure of the aspect in sentence. Then, a structural weighted-layer is applied after LSTM. In this situation, the syntactically relevant context is formulated. Besides, the sequential position is combined to reduce the impact of noise caused by imperfect grammatical analysis tools. SPNet not only significantly improves the ability of encoding syntactical information and word dependencies, but also provides a tailor-made representation for different aspect in a sentence. On three public ABSC datasets, SPNet produces a competitive performance compared with some existing state-of-the-art methods.