Abstract
Aspect-level sentiment analysis aims to predict the sentiment polarity of a given target in a review sentence. Most of the previous methods focus on capturing the context information of words across the sentence related to the target, ignoring the importance of the independent relationship between the opinion words and the target. To address this limitation, we propose a position-aware hybrid attention network model for aspect-level sentiment analysis, which incorporates not only the context information of words related to the target, but also the independent relationship between the opinion words related to the target. We conduct several comparable experiments on public laptop and restaurant datasets. The experimental results show that our proposed model achieves a more effective performance than the baseline models.
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Acknowledgements
This work is supported by National Nature Science Foundation of China (61976062), the Science and Technology Program of Guangzhou, China (No. 201904010303 and No. 202002030227) and the Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation (“Climbing Program” Special Funds, grant number: pdjh2019b0173).
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Zheng, Y., Li, X., Su, G., Ma, J., Ning, C. (2020). Position-aware Hybrid Attention Network for Aspect-Level Sentiment Analysis. In: Dou, Z., Miao, Q., Lu, W., Mao, J., Jia, G. (eds) Information Retrieval. CCIR 2020. Lecture Notes in Computer Science(), vol 12285. Springer, Cham. https://doi.org/10.1007/978-3-030-56725-5_7
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DOI: https://doi.org/10.1007/978-3-030-56725-5_7
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