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Text Classification Algorithms: A Survey

Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
Sensing Systems for Health Lab, University of Virginia, Charlottesville, VA 22911, USA
School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
Author to whom correspondence should be addressed.
Information 2019, 10(4), 150;
Received: 22 March 2019 / Revised: 17 April 2019 / Accepted: 17 April 2019 / Published: 23 April 2019
(This article belongs to the Special Issue Machine Learning on Scientific Data and Information)
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 real-world problems are discussed. View Full-Text
Keywords: text classification; text mining; text representation; text categorization; text analysis; document classification text classification; text mining; text representation; text categorization; text analysis; document classification
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Kowsari, K.; Jafari Meimandi, K.; Heidarysafa, M.; Mendu, S.; Barnes, L.; Brown, D. Text Classification Algorithms: A Survey. Information 2019, 10, 150.

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