Enhancing Aspect-Based Sentiment Analysis Systems through Edge-Enhanced Graph Convolutional Networks

The core message of this study is to enhance the performance of aspect-based sentiment analysis systems by leveraging an edge-enhanced bidirectional graph convolutional network (Bi-GCN) called SentiSys. SentiSys combines Bi-LSTM, a transformer encoder, and Bi-GCN to effectively capture syntactic dependencies, global contextual information, and aspect-specific sentiment features.