Abstract
Graph Convolutional Network (GCN) for aspect-based sentiment classification has attracted a lot of attention recently due to their promising performance in handling complex structure information. However, previous methods based on GCN focused mainly on examining the structure of syntactic dependency relationships, which were subject to the noise and sparsity problem. Furthermore, these methods tend to focus on one kind of structural information (namely syntactic dependency) while ignoring many other kinds of rich structures between words. To tackle these problems, we propose a novel GCN based model, named Structure-Enhanced Dual-Channel Graph Convolutional Network (SEDC-GCN). Specifically, we first exploit the rich structure information by constructing a text sequence graph and an enhanced dependency graph, then design a dual-channel graph encoder to model the structure information from the two graphs. After that, we propose two kinds of aspect-specific attention, i.e., aspect-specific semantic attention and aspect-specific structure attention, to learn sentence representation from two different perspectives, i.e., the semantic perspective based on the text encoder, and the structure perspective based on the dual-channel graph encoder. Finally, we merge the sentence representations from the above two perspectives and obtain the final sentence representation. We experimentally validate our proposed model SEDC-GCN by comparing with seven strong baseline methods. In terms of the metric accuracy, SEDC-GCN achieves performance gains of 74.42%, 77.74%, 83.30%, 81.73% and 90.75% on TWITTER, LAPTOP, REST14, REST15, and REST16, respectively, which are 0.35%, 4.22%, 1.62%, 0.70% and 2.01% better than the best performing baseline BiGCN. Similar performance improvements are also observed in terms of the metric macro-averaged F1 score. The ablation study further demonstrates the effectiveness of each component of SEDC-GCN.
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Data Availability Statement
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China [grant number 62141201]; and the Federal Ministry of Education and Research [grant number 01IS21086].
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Zhu, L., Zhu, X., Guo, J. et al. Exploring rich structure information for aspect-based sentiment classification. J Intell Inf Syst 60, 97–117 (2023). https://doi.org/10.1007/s10844-022-00729-1
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DOI: https://doi.org/10.1007/s10844-022-00729-1