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In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect. Based on it, we propose a novel graph-aware model with Interactive Graph Convolutional Networks (InterGCN) for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntacticaldoi:10.5281/zenodo.4147291 fatcat:2yvdgcm27fb3lcx4k75pbmxsqu