research-article

Deep Learning--based Text Classification: A Comprehensive Review

Authors:

Shervin Minaee,

Nal Kalchbrenner,

Meysam Chenaghlu, and

Jianfeng GaoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 3

Article No.: 62, Pages 1 - 40

https://doi.org/10.1145/3439726

Published: 17 April 2021 Publication History

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.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 3

April 2022

836 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3461619

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Published in CSUR Volume 54, Issue 3

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