The DOI matches its Crossref record; 150 of the 150 references it cites were found in a registry or an index.
A Survey on Text Classification: From Traditional to Deep Learning
Abstract
Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
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Kalyan, K. S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100048 · Cited by 335
Artificial intelligence in multimodal learning analytics: a systematic literature review
Mohammadi, M., Tajik, E., Martinez-Maldonado, R., Sadiq, S., Tomaszewski, W., & Khosravi, H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100426 · Cited by 85
Predicting retracted research: a dataset and machine learning approaches
Fletcher, A. H. A., & Stevenson, M. (2025) · Research Integrity and Peer Review · doi:10.1186/s41073-025-00168-w · Cited by 9
Yilmaz, M., Gauthier, A., & Cukurova, M. (2026) · Educational technology research and development · doi:10.1007/s11423-026-10595-1 · Cited by 9
“Recall, on the other hand, calculates how effectively all true positive instances in the feedback are identified (Li et al., 2022).”
Metadata
- Authors
- Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L.
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2022-04-08
- Volume
- 13
- Issue
- 2
- Pages
- 1-41
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W4226418765
- Cited by
- 494
- References
- 150
- FWCI
- 35.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2022). A Survey on Text Classification: From Traditional to Deep Learning. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3495162
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