Relational Graph Convolutional Networks for Sentiment Analysis

TL;DR

This paper introduces a novel approach using Relational Graph Convolutional Networks combined with pre-trained language models to improve sentiment analysis by capturing complex entity relationships in user-generated content.

Contribution

It presents a new method integrating RGCNs with models like BERT and RoBERTa for enhanced sentiment analysis, demonstrating improved relational understanding.

Findings

RGCNs improve sentiment classification accuracy.

The approach effectively captures entity dependencies.

Enhanced interpretability of sentiment models.

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.