Relational Graph Convolutional Networks for Sentiment Analysis

arXiv:2404.13079v1 Announce Type: cross
Abstract: 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 …

abstract analysis arxiv become cs.cl cs.lg data deep learning generated graph growth insights networks online platforms paper platforms relational relationships sentiment sentiment analysis textual type