Enhancing the Performance of Aspect-Based Sentiment Analysis Systems

arXiv:2404.03259v1 Announce Type: new
Abstract: Aspect-based sentiment analysis aims to predict sentiment polarity with fine granularity. While Graph Convolutional Networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromise information preservation. This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigate the syntactic graph while preserving intact feature information, leading to enhanced performance. Specifically,we first integrate a bidirectional long short-term memory (Bi-LSTM) network and a self-attention-based transformer. This combination facilitates effective …

abstract analysis application arxiv cs.ai cs.cl edge extraction feature feature extraction graph information networks performance preservation sentiment sentiment analysis study systems type