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Authors = Sanjana Mendu

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68 pages, 7541 KiB  
Review
Text Classification Algorithms: A Survey
by Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura Barnes and Donald Brown
Information 2019, 10(4), 150; https://doi.org/10.3390/info10040150 - 23 Apr 2019
Cited by 1230 | Viewed by 109273
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Machine Learning on Scientific Data and Information)
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