Abstract
Aspect-level sentiment classification aims to identify the polarity of a target word in a sentence. Studies on sentiment classification have found that a target’s surrounding words have great impacts and global attention to the target. However, existing neural-network-based models either depend on expensive phrase-level annotation or do not fully exploit the association of the context words to the target. In this paper, we propose to model the influences of the target’s surrounding words via two unidirectional long short-term memory neural networks, and introduce a target-based attention mechanism to discover the underlying relationship between the target and the context words. Empirical results on the SemEval 2014 Datasets show that our approach outperforms many competitive sentiment classification baseline methods. Detailed analysis demonstrates the effectiveness of the proposed surrounding-based long-short memory neural networks and the target-based attention mechanism.
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Notes
- 1.
Detailed information can be found at: http://alt.qcri.org/semeval2014/task4.
- 2.
Available at: https://nlp.stanford.edu/projects/glove/.
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Acknowledgments
This work is supported by the Humanities and Social Sciences Fund of Ministry of Education (13YJC870023) and the National Social Science Fund of China (15BTQ056).
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Sun, Y., Wang, X., Liu, H., Wang, W., Jiao, P. (2019). Surrounding-Based Attention Networks for Aspect-Level Sentiment Classification. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_13
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