The DOI matches its Crossref record; 142 of the 142 references it cites were found in a registry or an index.
Text Classification Algorithms: A Survey
Open accessAbstract
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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Ahmad, S. F., Alam, M. M., Rahmat, M. K., Mubarik, M. S., & Hyder, S. I. (2022) · Sustainability · doi:10.3390/su14031101 · Cited by 309
Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3434237 · Cited by 194
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Metadata
- Authors
- Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D.
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2019-04-23
- Volume
- 10
- Issue
- 4
- Pages
- 150
- ISSN
- 2078-2489 (ISSN-L)
- OpenAlex
- W2937423263
- Cited by
- 1,312
- References
- 142
- FWCI
- 64.01
- Type
- journal article
- Language
- en
- Open access
- gold
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