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Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanism

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Abstract

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years. It involves several subtasks for extracting one or more sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. In this paper, we propose two novel approaches to addressing different ABSA subtasks. Firstly, we introduce a Dependency-Attention GCN-based Aspect Opinion Extractor (DAG-AOE) for the Aspect-Opinion Pair Extraction (AOPE) task. DAG-AOE employs an improved graph convolutional network to extract syntactic structure information from text sequences, thereby effectively identifying aspect-opinion pairs in sentences. Secondly, we propose a Mutual Assistance Mechanism-based Category Sentiment Classifier (MAM-CSC) that utilizes the results of DAG-AOE to address the Aspect Sentiment Quad Prediction (ASQP) task. MAM-CSC leverages the semantic relationships between words in a sentence and addresses the two independent classification tasks through a mutual assistance approach. We conduct extensive experiments on benchmark datasets, and the experimental results demonstrate that our models have demonstrated a significant improvement over all baseline methods. Specifically, our models achieved the highest improvement of 1.4% F1 score over the baseline on the AOPE task and 1.5% F1 score on the ASQP task.

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Data Availability

All the datasets gathered from other sources has been publicly available.

Notes

  1. https://spacy.io/

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Acknowledgements

This work was supported by the High Performance Computing Center of Central South University and the National Natural Science Foundation of China under Grants: Methodologies for Understanding Big Data and Knowledge Discovery (61836016).

Funding

This work was supported by the National Natural Science Foundation of China under Grants: Methodologies for Understanding Big Data and Knowledge Discovery (61836016).

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  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Jialin Feng, Hong Li & Zhiyi Yu

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  1. Jialin Feng
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  2. Hong Li
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Contributions

Jialin Feng: Conceptualization, Investigation, Methodology, Software, Formal Analysis, Writing; Hong Li: Conceptualization, Funding Acquisition, Resources, Supervision; Zhiyi Yu: Investigation, Software, Visualization.

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Correspondence to Jialin Feng.

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Feng, J., Li, H. & Yu, Z. Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanism. J Intell Inf Syst 62, 163–189 (2024). https://doi.org/10.1007/s10844-023-00811-2

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