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research-article

A novel adaptive marker segmentation graph convolutional network for aspect-level sentiment analysis

Authors:

Pengcheng Wang,

Kezhu MengAuthors Info & Claims

Volume 270, Issue C

https://doi.org/10.1016/j.knosys.2023.110559

Published: 21 June 2023 Publication History

Abstract

Aspect-level sentiment analysis is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in online reviews. Attention mechanisms and graph convolutional networks have recently been widely used to model associations between aspects and opinion words. However, these methods face challenges in accurately modeling the alignment of aspects and exploiting multiaspect sentiment dependencies due to the limitations of dependency trees and the complexities of online reviews. In this paper, we propose a novel adaptive marker segmentation graph convolutional network (AMS-GCN) for aspect-level sentiment analysis. Specifically, the proposed AMS-GCN model enhances the information capacity of words by merging marker information from two datasets and uses an adaptive marker segmentation module to divide different marker information into separate modules. Furthermore, the model employs bi-syntax-aware and semantic auxiliary modules to obtain syntactic and semantic information. The bi-syntax-aware module combines component and dependency trees to capture comprehensive syntactic information. In contrast, the semantic auxiliary module uses an attention score matrix to capture the semantic association information of each word. Moreover, the aspect-related graph is devised to aggregate information about the sentiment of different aspects. Experiments on several benchmark datasets demonstrate that the proposed model achieves state-of-the-art results.

Highlights

•

We proposed a novel adaptive marker segmentation graph convolutional network (AMS-GCN) for aspect-level sentiment analysis.

•

We use the data merging module (DM) to expand the data during the data pre-processing phase, then use the AMS module to adaptively partition the data.

•

We introduce a two-channel GCN for extracting accurate syntactic and semantic information. And the syntactic information and semantic information are extracted using bi-syntax-aware and semantic auxiliary modules, respectively.

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Cited By

Yu BCao CYang Y(2025)Dynamic position weighting aspect-focused graph convolutional network for aspect-based sentiment analysisThe Journal of Supercomputing10.1007/s11227-024-06783-681:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06783-6
Kandhro IAli FUddin MKehar AManickam S(2024)Exploring aspect-based sentiment analysis: an in-depth review of current methods and prospects for advancementKnowledge and Information Systems10.1007/s10115-024-02104-866:7(3639-3669)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10115-024-02104-8
Liu GJiang WHu YZhan KWang T(2023)CGIR: a Model of Cross Language Information Retrieval based on Concept Graph by Fusing Attention MechanismProceedings of the 2023 6th International Conference on Information Science and Systems10.1145/3625156.3625157(1-7)Online publication date: 11-Aug-2023
https://dl.acm.org/doi/10.1145/3625156.3625157

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Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 270, Issue C

Jun 2023

287 pages

ISSN:0950-7051

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 21 June 2023

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Cited By

Yu BCao CYang Y(2025)Dynamic position weighting aspect-focused graph convolutional network for aspect-based sentiment analysisThe Journal of Supercomputing10.1007/s11227-024-06783-681:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06783-6
Kandhro IAli FUddin MKehar AManickam S(2024)Exploring aspect-based sentiment analysis: an in-depth review of current methods and prospects for advancementKnowledge and Information Systems10.1007/s10115-024-02104-866:7(3639-3669)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10115-024-02104-8
Liu GJiang WHu YZhan KWang T(2023)CGIR: a Model of Cross Language Information Retrieval based on Concept Graph by Fusing Attention MechanismProceedings of the 2023 6th International Conference on Information Science and Systems10.1145/3625156.3625157(1-7)Online publication date: 11-Aug-2023
https://dl.acm.org/doi/10.1145/3625156.3625157

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