Deep Learning Based Text Classification: A Comprehensive Review

TL;DR

This paper reviews over 150 deep learning models for text classification, analyzing their technical contributions, datasets, and performance, and discusses future research directions in the field.

Contribution

It provides a comprehensive survey of recent deep learning models for text classification, including technical insights, dataset summaries, and performance analysis.

Findings

Deep learning models outperform classical methods in text classification.

Performance varies significantly across different models and datasets.

Future research should focus on model robustness and dataset diversity.

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 paper, we provide a comprehensive review of more than 150 deep learning based models for text classification developed in recent years, and 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 discuss future research directions.