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A multichannel fusion learning model with syntax for Chinese-oriented aspect-level sentiment classification

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Abstract

Aspect-level sentiment classification (ASC), as a classic fine-grained sentiment analysis task, aims to analyze the sentiment polarity of different aspect terms in a sentence or document, which is a subtask of Aspect-based sentiment analysis (ABSA). Although many models of ASC have been proposed, they fail to effectively identify the range of local context and cannot fully and effectively utilize the semantic information of the global context. Moreover, there are few studies on Chinese ASC tasks. In this work, a multichannel fusion learning model with syntax for Chinese-oriented aspect-level sentiment classification is proposed. It uses the syntactic relative distance (SRD) computed based on a dependency syntax parsing tree to represent semantic distance. In addition, the model effectively integrates the semantic information of local context channel, global context channel and dependency tree channel, thereby improving the performance of the model. Compared with existing models, the proposed model can identify the range of local context more accurately and better explain the syntactic correlation between aspect terms and local context. Furthermore, the model can accurately analyze Chinese and English reviews. Experiment results on six English benchmark datasets and three Chinese benchmark datasets show that our model outperforms the baseline model and has better interpretability.

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Availability of data and materials

Data supporting the results of this study are available upon request from the author hejiangtao2024@163.com. Because they contain information that may compromise the consent of study participants, these data are not publicly available.

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Acknowledgements

We thank the editor and reviewers for their valuable comments to improve the quality of this article.

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Authors and Affiliations

  1. School of Electronics and Communication Engineering, Zhejiang Post And Telecommunication College, Shaoxing, 312000, Zhejiang, China

    Jiangtao He

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  1. Jiangtao He
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JH contributed to investigation, data curation, conceptualization, methodology, writing—original draft, writing—review and editing, and supervision.

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Correspondence to Jiangtao He.

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He, J. A multichannel fusion learning model with syntax for Chinese-oriented aspect-level sentiment classification. J Supercomput 81, 200 (2025). https://doi.org/10.1007/s11227-024-06674-w

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