An effective graph attention network for aspect-based sentiment analysis

WeiHua Du; Guangming Xian

doi:10.1117/12.2685032

19 October 2023 An effective graph attention network for aspect-based sentiment analysis

WeiHua Du, Guangming Xian

Author Affiliations +

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090A (2023) https://doi.org/10.1117/12.2685032
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Aspect Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity of a review towards a given aspect terms. One main challenge of this task is to correlate relevant opinion words with aspect terms, especially for the aspect terms consisting multi words. To this end, attention mechanism plays an important role in previous models due to its capability of modeling the relationship between the context and aspect words, and solving the defect of parallelization which can't be realized in conventional neural network like recurrent neural network. However, the attention mechanism may introduce irrelevant noise and reduce models’ performance. To alleviate the above problem, graph neural network is proposed which utilize the syntactic information for ABSA task. Following this line, we propose a semantic enhancement graph attention network model named SEN-GAT, which innovatively introduce the aggregation and aspect-aware attention mechanism. The experimental results on standard datasets show that our proposed model can substantially improve ABSA tasks performance.

Citation Download Citation

WeiHua Du and Guangming Xian "An effective graph attention network for aspect-based sentiment analysis", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090A (19 October 2023); https://doi.org/10.1117/12.2685032

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