Advancing Aspect-Based Sentiment Analysis through Deep Learning Models

TL;DR

This paper introduces SentiSys, a novel deep learning model combining Bi-LSTM, transformer, and an edge-enhanced Bi-GCN with masking to improve aspect-based sentiment analysis by better capturing syntactic and semantic features.

Contribution

The paper presents an innovative edge-enhanced GCN model, SentiSys, that effectively preserves syntactic information and improves sentiment analysis accuracy over existing methods.

Findings

Enhanced performance on four benchmark datasets

Effective preservation of syntactic features

Improved sentiment prediction accuracy

Abstract

Aspect-based sentiment analysis predicts 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 text encoding, preventing the loss of information and predicting long dependency text. A bidirectional GCN (Bi-GCN) with message passing is then employed to encode relationships between entities. Additionally, unnecessary information is filtered out using an aspect-specific masking technique. To validate the effectiveness of our proposed model, we conduct extensive evaluation experiments on four benchmark datasets. The experimental results demonstrate enhanced performance in aspect-based sentiment analysis with the use of SentiSys.