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A Comprehensive Review of Text Classification Algorithms

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DOI: 10.23977/jeis.2024.090205 | Downloads: 17 | Views: 210


Yachang Song 1, Xinyu Liu 1, Ze Zhou 1


1 College of Electronic Information, Xijing University, Xian, China

Corresponding Author

Yachang Song


This paper reviews the development of text classification algorithms, from rule-based and traditional machine learning methods to the evolution of deep learning and pre-trained models. As a crucial aspect of natural language processing, text classification is essential for various applications, such as information retrieval and sentiment analysis. The application of deep learning models like CNNs, RNNs, and pre-trained models such as BERT in text classification is highlighted, showcasing their advantages in processing large corpora. The challenges and future research directions in text classification are also discussed, offering guidance to researchers and practitioners in the field.


Text Classification, Deep Learning, Pre-trained Models, Natural Language Processing, CNN, RNN, BERT


Yachang Song, Xinyu Liu, Ze Zhou, A Comprehensive Review of Text Classification Algorithms. Journal of Electronics and Information Science (2024) Vol. 9: 34-42. DOI:


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