Text Classification Algorithms: A Survey

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 natura...

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Bibliographic Details
Published in:Information (Basel) Vol. 10; no. 4; p. 150
Main Authors: Kowsari, Kamran, Jafari Meimandi, Kiana, Heidarysafa, Mojtaba, Mendu, Sanjana, Barnes, Laura, Brown, Donald
Format: Journal Article
Language:English
Published: Basel MDPI AG 23.04.2019
Subjects:
Algorithms
Classification
Data mining
Datasets
Deep learning
Discriminant analysis
document classification
Feature extraction
Information retrieval
Knowledge management
Machine learning
Natural language processing
Pipelines
Principal components analysis
text analysis
Text categorization
text classification
Text messaging
text mining
text representation
Texts
ISSN:2078-2489, 2078-2489
Online Access:Get full text
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Summary: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.
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ISSN:2078-2489
2078-2489
DOI:10.3390/info10040150