Abstract
With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we propose to use a novel framework named as recurrent graph convolutional network (RGCN) for target-dependent sentiment classification in which the given text is considered as a graph based on its syntactic structure and recurrent graph convolutional networks are used to encode the text and target. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines.
Supported by Inner Mongolia Natural Science Foundation of China (2018MS06005, 2015MS0628) and Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry.
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Chen, J., Hou, H., Gao, J., Ji, Y., Bai, T. (2019). RGCN: Recurrent Graph Convolutional Networks for Target-Dependent Sentiment Analysis. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_59
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