Text Classification Based on Machine Learning: A Review  

作　　者：Yang Sun 

机构地区：[1]Software College,Shenyang Normal University 110034 Shenyang,China

出　　处：《IJLAI Transactions on Science and Engineering》2025年第1期45-51,共7页IJLAI科学与工程学报汇刊(英文)

摘　　要：With the rapid development of the Internet, text information has shown a blowout growth. Massivetext data such as news, social media posts, academic literature, etc. are constantly emerging, and manual classificationand management of these texts has become time-consuming and inefficient, which is difficult to meetthe actual needs. The continuous progress of natural language processing technology, especially the rise of deeplearning methods, provides strong technical support for automatic text classification. Deep learning models canautomatically mine the essential features of text from massive samples, capture deep semantic representationinformation, and avoid the tedious process of manual design rules and features. In practical applications, textdata often co-exists with data of other modes (such as images, audio, etc.). Through the feature learning ofmultimodal data, the information of multiple modes can be mapped to the joint vector space, and the unifiedrepresentation of data can be obtained, so that the text classification can be more accurate. In recent years,pre-trained language models such as BERT and GPT have achieved remarkable results. These models learn acommon language representation through unsupervised pre-training on large-scale corpus, and then fine-tuneon specific text classification tasks, which can significantly improve the classification performance and furtherpromote the research of automatic text classification. Automatic text classification can classify massive textdata into different categories quickly and accurately, which is convenient for information storage, retrievaland management. For example, in the fields of library document management and enterprise document management,automatic classification can greatly improve work efficiency and save labor costs. In social mediaand online public opinion monitoring, automatic text classification can quickly identify text information withdifferent themes and emotional tendencies. This helps to timely understand the dynamics of public opinion,an

关 键 词：Automatic text classification Machine learning Pre-trained language model 

分 类 号：H31[语言文字—英语]