Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis problem, which has attracted much attention in recent years. Previous methods mainly devote to employing attention mechanism to model the relationship between aspects and context words. However, these methods tend to ignore the overall semantics of sentence and dependency among the aspect terms. In this paper, we propose a Hierarchical Attention Network (HAN) to solve the aforementioned issues simultaneously. Experimental results on standard SemEval 2014 datasets demonstrate the effectiveness of the proposed model.
This paper is supported by the National Key Research and Development Program of China (Grant No. 2016YFB1001102), the National Natural Science Foundation of China (Grant Nos. 61876080, 61502227), the Fundamental Research Funds for the Central Universities No.020214380040, the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University.
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Notes
- 1.
We run two LSTM forwards and backwards respectively and concatenate the two hidden vectors in i-th time step to produce \(h_i^a\).
- 2.
It is worth noting that RAM utilizes additional position information.
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Ran, X., Pan, Y., Sun, W., Wang, C. (2019). Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_76
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DOI: https://doi.org/10.1007/978-3-030-18590-9_76
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