Research on the application of graph neural networks in text classification

Abstract

With the rapid development of the Internet and social media, a large amount of text data is constantly generated, and text classification has become an important task. Text classification is an important branch in the field of natural language processing and has attracted the attention of many researchers in recent years. Due to the ease of editing text data, most texts in the network are manually constructed and uploaded by users. Therefore, the standardization of online texts and the classification of texts with different granularity are of great significance in the field of information retrieval. This article introduces the basic concepts of text classification and three text classification methods: knowledge engineering (rule) based text classification methods, traditional machine learning-based text classification methods, and deep neural network-based text classification methods. This article also analyzes the advantages and disadvantages of the three methods. Subsequently, the basic concepts and common models of graph neural networks were explored. An application case of graph neural networks in text classification is provided. This article provides effective suggestions for text classification applications in the field of natural language processing.