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Enhanced dual-level dependency parsing for aspect-based sentiment analysis

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Abstract

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, aiming at mining sentiment polarity towards specific aspects. Most existing work to address ABSA has focused on using Graph Neural Networks combined with syntactic dependency trees. However, existing models often fall into semantic confusion for sentiment analysis due to the information imbalance in the dependency tree. To solve the problem of semantic confusion, we propose a Local Enhanced Relational Graph Attention Network with Dual-level Dependency Parsing (DL-RGAT) model. The dual-level dependency parsing structure constructs a dependency grid for each aspect word, which contains only dependency relations that are related strongly to the aspect. It effectively isolates the negative impact from irrelevant words near the aspect on the aspect. Then the proposed Gaussian local context dynamic weighting structure adaptively adjusts the feature weights of the local context and filters the negative impact of local contexts far from the aspect on the aspect. In this way, the semantic confusion problem is effectively solved. Finally, the parsed dependency relations are encoded for sentiment analysis using a relational graph attention network. Extensive experiments on benchmark datasets have shown that DL-RGAT improves 1.44–5.24% and 1.64–6.7% in average accuracy and average Macro-F1 compared to the results of state-of-the-art studies over the past 3 years.

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Data availability

Data supporting the results of this study are available upon request from the corresponding author liulisha0028@mails.ccnu.edu. Because they contain information that may compromise the consent of study participants, these data are not publicly available.

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Authors and Affiliations

  1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, China

    Maoyuan Zhang & Lisha Liu

  2. School of Computer, Central China Normal University, Wuhan, Hubei, 430079, China

    Maoyuan Zhang, Lisha Liu, Jiaxin Mi & Xianqi Yuan

  3. National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, China

    Xianqi Yuan

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  1. Maoyuan Zhang
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  2. Lisha Liu
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  3. Jiaxin Mi
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Correspondence to Lisha Liu.

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Zhang, M., Liu, L., Mi, J. et al. Enhanced dual-level dependency parsing for aspect-based sentiment analysis. J Supercomput 79, 6290–6308 (2023). https://doi.org/10.1007/s11227-022-04898-2

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  • DOI: https://doi.org/10.1007/s11227-022-04898-2

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