Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification

• URL: http://arxiv.org/abs/2503.12803v1
• Date: Mon, 17 Mar 2025 04:19:20 GMT
• Title: Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification
• Authors: Chen Li, Debo Cheng, Yasuhiko Morimoto,
• Abstract summary: EEGCN improves performance by preserving feature integrity as it processes syntactic graphs.<n>We incorporate a bidirectional long short-term memory (Bi-LSTM) network alongside a self-attention-based transformer for effective text encoding.<n> Extensive evaluations and ablation studies on four benchmark datasets show that EEGCN significantly enhances aspect-based sentiment analysis.
• Score: 4.9754736060147415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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