Abstract
Aspect-level sentiment analysis has identified its significance in sentiment polarity classification of consumer review. For the purpose of specific target sentiment analysis, we put forward a co-attentive deep learning method in the manner of human processing. To start with, the GRUs are taken to extract the hidden states of the different word embeddings. Further, via the interactive learning of the co-attention network, the representations of the target and the context can be obtained. In addition, the attention weights are determined based on the self-attention mechanism to update the final representations. The experimental results evaluated on the SemEval 2014 and Twitter establish a strong evidence of the high accuracy.
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Notes
- 1.
The detail introduction of this task can be seen at: http://alt.qcri.org/semeval2014/task4/.
- 2.
Pre-trained word vectors of Glove can be obtained from http://nlp.stanford.edu/projects/glove/.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 61876205, and the Science and Technology Plan Project of Guangzhou under Grant Nos. 201802010033 and 201804010433.
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Li, H., Xue, Y., Zhao, H., Hu, X., Peng, S. (2019). Co-attention Networks for Aspect-Level Sentiment Analysis. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_17
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