Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis

Abstract

The state-of-the-art methods in aspect-level sentiment classification have leveraged the graph based models to incorporate the syntactic structure of a sentence. While being effective, these methods ignore the corpus level word co-occurrence information, which reflect the collocations in linguistics like ``nothing special''. Moreover, they do not distinguish the different types of syntactic dependency, e.g., a nominal subject relation ``food-was'' is treated equally as an adjectival complement relation ``was-okay'' in ``food was okay''. To tackle the above two limitations, we propose a novel architecture which convolutes over hierarchical syntactic and lexical graphs. Specifically, we employ a global lexical graph to encode the corpus level word co-occurrence information. Moreover, we build a concept hierarchy on both the syntactic and lexical graphs for differentiating various types of dependency relations or lexical word pairs. Finally, we design a bi-level interactive graph convolution network to fully exploit these two graphs. Extensive experiments on five bench- mark datasets show that our method outperforms the state-of-the-art baselines.

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Reproductions