Deep Learning–based Text Classification: A Comprehensive Review. Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. ACM Comput. Surv., Association for Computing Machinery, New York, NY, USA, apr, 2021.
Deep Learning–based Text Classification: A Comprehensive Review [link]Paper  doi  abstract   bibtex   
Deep learning–based models have surpassed classical machine learning–based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning–based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.
@article{10.1145/3439726,
author = {Minaee, Shervin and Kalchbrenner, Nal and Cambria, Erik and Nikzad, Narjes and Chenaghlu, Meysam and Gao, Jianfeng},
title = {Deep Learning--based Text Classification: A Comprehensive Review},
year = {2021},
issue_date = {April 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {54},
number = {3},
issn = {0360-0300},
url = {https://doi.org/10.1145/3439726},
doi = {10.1145/3439726},
abstract = {Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.},
journal = {ACM Comput. Surv.},
month = {apr},
articleno = {62},
numpages = {40},
keywords = {Text classification, deep learning, natural language inference, news categorization, question answering, sentiment analysis, topic classification}
}

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