Text Classification is the most essential and fundamental problem in Natural
Language Processing. While numerous recent text classification models applied
the sequential deep learning technique, graph neural network-based models can
directly deal with complex structured text data and exploit global information.
Many real text classification applications can be naturally cast into a graph,
which captures words, documents, and corpus global features. In this survey, we
bring the coverage of methods up to 2023, including corpus-level and
document-level graph neural networks. We discuss each of these methods in
detail, dealing with the graph construction mechanisms and the graph-based
learning process. As well as the technological survey, we look at issues behind
and future directions addressed in text classification using graph neural
networks. We also cover datasets, evaluation metrics, and experiment design and
present a summary of published performance on the publicly available
benchmarks. Note that we present a comprehensive comparison between different
techniques and identify the pros and cons of various evaluation metrics in this
survey.

本文介绍文本分类的方法，重点讨论基于图神经网络的文本分类，分别从构造机制和学习过程等方面对文本分类方法进行详细介绍，并涵盖数据集，评估指标和实验设计等方面，通过对公开基准测试的综合比较评估和评估指标的优缺点比较，总结出各个技术的优缺点。