Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910878123687936 |
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| author | Li, Chen Cheng, Debo Morimoto, Yasuhiko |
| author_facet | Li, Chen Cheng, Debo Morimoto, Yasuhiko |
| contents | Aspect-based sentiment analysis seeks to determine sentiment with a high level of detail. While graph convolutional networks (GCNs) are commonly used for extracting sentiment features, their straightforward use in syntactic feature extraction can lead to a loss of crucial information. This paper presents a novel edge-enhanced GCN, called EEGCN, which improves performance by preserving feature integrity as it processes syntactic graphs. We incorporate a bidirectional long short-term memory (Bi-LSTM) network alongside a self-attention-based transformer for effective text encoding, ensuring the retention of long-range dependencies. A bidirectional GCN (Bi-GCN) with message passing then captures the relationships between entities, while an aspect-specific masking technique removes extraneous information. Extensive evaluations and ablation studies on four benchmark datasets show that EEGCN significantly enhances aspect-based sentiment analysis, overcoming issues with syntactic feature extraction and advancing the field's methodologies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_12803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification Li, Chen Cheng, Debo Morimoto, Yasuhiko Computation and Language Machine Learning Aspect-based sentiment analysis seeks to determine sentiment with a high level of detail. While graph convolutional networks (GCNs) are commonly used for extracting sentiment features, their straightforward use in syntactic feature extraction can lead to a loss of crucial information. This paper presents a novel edge-enhanced GCN, called EEGCN, which improves performance by preserving feature integrity as it processes syntactic graphs. We incorporate a bidirectional long short-term memory (Bi-LSTM) network alongside a self-attention-based transformer for effective text encoding, ensuring the retention of long-range dependencies. A bidirectional GCN (Bi-GCN) with message passing then captures the relationships between entities, while an aspect-specific masking technique removes extraneous information. Extensive evaluations and ablation studies on four benchmark datasets show that EEGCN significantly enhances aspect-based sentiment analysis, overcoming issues with syntactic feature extraction and advancing the field's methodologies. |
| title | Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2503.12803 |