Joint learning of aspect term and aspect category sentiment analysis with BERT-based stacking models

Wei Zhang; Zimo Yin; Jinke Liu; Jianchiang Ma; Derong Zheng; Xuan Li

doi:10.1117/12.3026942

8 April 2024 Joint learning of aspect term and aspect category sentiment analysis with BERT-based stacking models

Wei Zhang, Zimo Yin, Jinke Liu, Jianchiang Ma, Derong Zheng, Xuan Li

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901X (2024) https://doi.org/10.1117/12.3026942
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

The two main tasks in aspect-based sentiment analysis are aspect term sentiment analysis and aspect category sentiment analysis. In this study, we adopt a unified learning approach to train these two tasks simultaneously, leading to an enhancement in their overall performance. We also introduce three BERT-based models to extract various aspects of semantic information from the context. The best results are achieved by combining these models using a stacking strategy. The experimental results on the MAMS dataset indicate that our model achieve an F1 score of 82.25%, surpassing the performance of neural baseline models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Zhang, Zimo Yin, Jinke Liu, Jianchiang Ma, Derong Zheng, and Xuan Li "Joint learning of aspect term and aspect category sentiment analysis with BERT-based stacking models", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901X (8 April 2024); https://doi.org/10.1117/12.3026942

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