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Text Classification Algorithms: A Survey

arXiv 1904.08067 ↗First posted 17 Apr 2019cs.LG cs.AI cs.CLcited by 163

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Abstract (arXiv)

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.

Key facts. Cited by 163 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Apr 2019.

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Citing papers per year: 2021: 28; 2022: 36; 2023: 41; 2024: 28; 2025: 23; 2026: 7.

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