Text Classification Method Based on Graph Neural Networks

Artificial Intelligence Department, Beijing Normal University, 100000, Beijing, China

^* Corresponding author: 202411081062@mail.bnu.edu.cn

Abstract

The goal of text classification is to assign labels to text units accurately, which is a basic task in natural language processing. This technology has shown great value in many practical application scenarios, covering spam detection, emotional tendency analysis, user intent recognition, and many other aspects. In recent years, because of the excellent performance of graph convolutional neural networks (GCNs) in processing non-European spatial data, it has become a research hotspot in text classification and has been widely adopted. This paper first introduces the background knowledge and working principle of graph convolutional neural networks. Then the text classification method based on GCNs is described. The last thing discussed is the limitations of graph convolutional neural networks, as well as the difficulties and future development directions in this field. This paper serves as a guide for researchers and practitioners in relevant fields to fully comprehend the latest advancements in text classification methods using graph convolutional neural networks, and by revealing its limitations and challenges, it also provides guidance for future research and development work, intending to promote technical progress and application expansion in this field.