Abstract
Aspect-based sentiment analysis (ABSA) refers to classifying the sentiment polarity of a specific aspect in a sentence. Recently, attention-based deep learning approaches are proposed to capture the semantic information and achieve satisfying results. However, due to the significance of syntactic structure, syntactic information is also analyzed for ABSA. As such, this work proposes a model that integrates the graph convolution network (GCN) and the co-attention mechanism to deal with the aspect-based information and remove the noise from unrelated context words. Both the semantic information and the syntactic information are conveyed by the representation for sentiment analysis. Experimental results show our model achieves a better working performance, which establishes a strong evidence of the capability.
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Acknowledgments
This work was supported by Humanity and Social Science Foundation of the Ministry of Education of China (21A13022003), Zhejiang Provincial Natural Science Fund (LY19F030010), Zhejiang Provincial Social Science Fund (20NDJC216YB), Zhejiang Educational Science Fund (GH2021642), the Science and Technology Plan Project of Guangzhou under Grant Nos. 202102080258 and 201903010013.
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Chen, Z., Xue, Y., Xiao, L., Chen, J., Zhang, H. (2021). Aspect-Based Sentiment Analysis Using Graph Convolutional Networks and Co-attention Mechanism. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_51
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