Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification
This work addresses a specific bottleneck in sentiment analysis for researchers and practitioners, offering an incremental advancement in methodology.
The paper tackled the problem of aspect-based sentiment analysis by proposing an edge-enhanced graph convolutional network (EEGCN) to preserve crucial information in syntactic feature extraction, resulting in significant performance improvements on four benchmark datasets.
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.