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    This study introduces a novel local-global interactive graph (LGIG) and DigNet model to enhance aspect-level sentiment classification (ASC). The approach integrates syntactic and relational information, significantly improving sentiment analysis performance.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Current aspect-level sentiment classification (ASC) models utilize syntax or relation graphs, each with limitations.
    • Existing methods fail to fully capture both local syntactic and global relational information effectively.
    • This gap hinders the overall representation power in graph modeling for ASC.

    Purpose of the Study:

    • To propose a novel Local-Global Interactive Graph (LGIG) that integrates syntax and relation graphs.
    • To introduce DigNet, a neural network designed to model the LGIG.
    • To improve the accuracy and effectiveness of aspect-level sentiment classification.

    Main Methods:

    • Developed a Local-Global Interactive Graph (LGIG) by connecting syntax and relation graphs with interactive edges.
    • Proposed DigNet, a neural network featuring stacked Local-Global Interactive (LGI) layers.
    • Implemented intragraph message passing (IGMP) and cross-graph message passing (CGMP) within LGI layers.

    Main Results:

    • The LGIG and DigNet model demonstrated superior performance on public benchmark datasets.
    • Achieved significant improvements in Macro-F1 scores: 3% on Lap14, 2.32% on Res14, and 6.33% on Res15.
    • Outperformed previous state-of-the-art methods in aspect-level sentiment classification.

    Conclusions:

    • The proposed LGIG effectively reconciles local syntactic and global relational information.
    • DigNet, by modeling LGIG, enhances the understanding of aspect-level sentiment.
    • The approach confirms the effectiveness and superiority of LGIG and DigNet for ASC tasks.