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 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.
Abstract (translated)
随着互联网上文本数据的快速增长，从用户生成内容中提取洞见的情味分析变得越来越重要。虽然传统方法和深度学习模型已经显示出巨大的潜力，但它们通常无法捕捉实体之间复杂的关系。在本文中，我们提出了一种利用关系图卷积网络（RGCNs）进行情感分析的方法，通过捕获数据点表示为图中节点的依赖关系来提供解释性和灵活性。我们通过使用预训练语言模型（如BERT和RoBERTa）的RGCN架构来评估亚马逊和Digikala数据集中的产品评论，以证明我们方法的有效性。我们的实验强调了RGCN在情感分析任务中捕获关系信息的有效性。
URL
https://arxiv.org/abs/2404.13079
