Relational Graph Convolutional Networks for Sentiment Analysis
This work addresses sentiment analysis for user-generated content, but it appears incremental as it combines existing methods like RGCNs with pre-trained models.
The paper tackled sentiment analysis by proposing Relational Graph Convolutional Networks (RGCNs) to capture complex relationships between entities, demonstrating effectiveness on product review datasets like Amazon and Digikala.
With the growth of textual data across online platforms, sentiment analysis has become crucial for extracting insights from user-generated content. While traditional approaches and deep learning models have shown promise, they cannot often capture complex relationships between entities. In this paper, we propose leveraging Relational Graph Convolutional Networks (RGCNs) for sentiment analysis, which offer interpretability and flexibility by capturing dependencies between data points represented as nodes in a graph. We demonstrate the effectiveness of our approach by using pre-trained language models such as BERT and RoBERTa with RGCN architecture on product reviews from Amazon and Digikala datasets and evaluating the results. Our experiments highlight the effectiveness of RGCNs in capturing relational information for sentiment analysis tasks.