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Enhancing aspect and opinion terms semantic relation for aspect sentiment triplet extraction

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Abstract

Aspect sentiment triplet extraction is the most recent subtask of aspect-based sentiment analysis, which aims to extract triplets information from a review sentence, including an aspect term, corresponding sentiment polarity, and associated opinion expression. Although existing researchers adopt an end-to-end method to avoid the error propagation caused by the pipeline manner, they cannot effectively establish the semantic association between aspects and opinions when extracting triples. Furthermore, utilizing sequence tagging methods in extraction and classification tasks will lead to problems, such as increased model search space and sentiment inconsistency of multi-word entities. To tackle the above issues, we propose an enhancing aspect and opinion terms semantic relation framework to make extract triplets more exact by fully capturing interactive information. Specifically, dual convolutional neural networks are used to construct aspect-oriented and opinion-oriented features respectively, the semantic relation is considered through the attention mechanism, and then feedback to each extraction task. We also employ a span-based tagging scheme to extract multiple entities directly under the supervision of span boundary detection accurately predict sentiment polarity based on span distance. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate that our model significantly outperforms all baseline methods.

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Notes

  1. https://github.com/xuuuluuu/SemEval-Triplet-data

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Acknowledgments

This work was supported in part by the National Social Science Foundation under Award 19BYY076, in part Key R & D project of Shandong Province 2019JZZY010129, and in part by the Shandong Provincial Social Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award 18BJYJ04.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Yongsheng Zhang, Qi Ding, Peiyu Liu & Fu Xie

  2. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China

    Zhenfang Zhu

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  1. Yongsheng Zhang
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  2. Qi Ding
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  3. Zhenfang Zhu
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  4. Peiyu Liu
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Correspondence to Peiyu Liu or Fu Xie.

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The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. All the datasets gathered from other sources has been publicly available.

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Zhang, Y., Ding, Q., Zhu, Z. et al. Enhancing aspect and opinion terms semantic relation for aspect sentiment triplet extraction. J Intell Inf Syst 59, 523–542 (2022). https://doi.org/10.1007/s10844-022-00710-y

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