Abstract
Aspect-based sentiment analysis (ABSA) is composed of aspect term sentiment analysis (ATSA) and aspect category sentiment analysis (ACSA). In the task of ACSA, some existing methods simply bound the aspect category (entity and attribute) as an integrated whole or adopt a randomly initialized embedding to represent the aspect category, which introduces a defective representation of aspect and leads to the ignorance of independent contextual sentiment of entity and attribute. Some other methods only consider the entity and disregard the attribute in predicting the sentiment polarity of aspect category, which leads to the ignorance of the collaboration between the entity and attribute. To this end, we propose a Gated Interactive Network (GIN) for aspect category sentiment analysis in this paper. To be specific, for each context and the corresponding aspect, we adopt two attention-based networks to learn the contextual sentiment for the entity and attribute independently and interactively. Further, based on the interactive attentions learned from entities and attributes, the coordinative gate units are exploited to reconcile and purify the sentiment features for the aspect sentiment prediction. Experimental results on two benchmark datasets demonstrate that our proposed model achieves state-of-the-art performance in the task of ACSA.
R. Yin and H. Su—Authors equally contributed to this work.
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Notes
- 1.
In this paper, we focus on the sentiment analysis of aspect category, i.e. aspect category sentiment analysis (ACSA), and the following “aspect” represents “aspect category”.
- 2.
We removed aspects express different sentiment polarities in the same review sentence.
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Acknowledgements
This work was supported by National Natural Science Foundation of China 61876053, 61632011, Shenzhen Foundational Research Funding JCYJ20180507183527919, JCYJ20180507183608379, Guangdong Province Covid-19 Pandemic Control Research Funding 2020KZDZX1224. We thank Dr. Lin Gui for valuable comments.
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Yin, R., Su, H., Liang, B., Du, J., Xu, R. (2020). Extracting the Collaboration of Entity and Attribute: Gated Interactive Networks for Aspect Sentiment Analysis. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_63
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