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Interactive memory networks based on syntactic dependencies for aspect-level sentiment classification

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Abstract

In recent years, sentiment analysis has emerged as a key area of research in natural language processing, with particular emphasis on aspect-level sentiment classification. This subfield focuses on identifying and analyzing sentiments expressed toward specific aspects within a sentence. Current approaches often combine aspect and context representations to evaluate sentiment, yielding commendable results. However, these methods typically overlook the importance of syntactic dependencies and tend to extract coarse representations of aspects and context, leading to interference from irrelevant information. To address these challenges, this study proposes an interactive memory network that leverages syntactic dependency information. By employing a graph convolutional network, the model captures richer contextual and aspect representations. Additionally, an interactive memory network module is designed to enhance the precise feature relationships between aspects and context. To evaluate the model’s effectiveness, comprehensive experiments were conducted on four widely used benchmark datasets, demonstrating its superior performance in sentiment classification tasks.

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Data Availability Statement

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.

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Authors and Affiliations

  1. Hunan Guo Tian Electronic Technology Co.,Ltd, Changsha, 410205, China

    Danqing Wu

  2. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Chao Zhu

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  1. Danqing Wu
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  2. Chao Zhu
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Danqing Wu provides the main innovation points, experiments, and paper writing. Chao Zhu offered paper writing and experiments.

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Correspondence to Chao Zhu.

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Wu, D., Zhu, C. Interactive memory networks based on syntactic dependencies for aspect-level sentiment classification. J Supercomput 81, 189 (2025). https://doi.org/10.1007/s11227-024-06594-9

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