Enhancing the Performance of Aspect-Based Sentiment Analysis Systems
Chen Li Jinli Zhang Huidong Tang Peng Ju Debo Cheng Yasuhiko Morimoto
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 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 and ablation studies on four benchmark
datasets. The results consistently demonstrate improved performance in
aspect-based sentiment analysis when employing SentiSys. This approach
successfully addresses the challenges associated with syntactic feature
extraction, highlighting its potential for advancing sentiment analysis
methodologies.