@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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