Text Classification Algorithms: A Survey

DOI Web Site 1 Citations
  • Kamran Kowsari
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
  • Kiana Jafari Meimandi
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
  • Mojtaba Heidarysafa
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
  • Sanjana Mendu
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
  • Laura Barnes
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
  • Donald Brown
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA

Description

<jats:p>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.</jats:p>

Journal

  • Information

    Information 10 (4), 150-, 2019-04-23

    MDPI AG

Citations (1)*help

See more

Details 詳細情報について

Report a problem

Back to top