Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation
This review article provides a thorough assessment of modern and innovative algorithms for text classification through both observational and experimental evaluations. We propose a new classification system, grounded in methodology, to categorize text classification algorithms into an organized stru...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-05-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020092 |
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| _version_ | 1839616861845585920 |
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| author | Kamal Taha Paul D. Yoo Chan Yeun Aya Taha |
| author_facet | Kamal Taha Paul D. Yoo Chan Yeun Aya Taha |
| author_sort | Kamal Taha |
| collection | DOAJ |
| description | This review article provides a thorough assessment of modern and innovative algorithms for text classification through both observational and experimental evaluations. We propose a new classification system, grounded in methodology, to categorize text classification algorithms into an organized structure from general categories down to particular fine-grained techniques. Drawing on more than 100 academic papers from prominent publishers, our extensive review spans a wide range of algorithms, encompassing traditional, deep learning, and emerging approaches. Through observational studies and comparative experiments among various algorithms, techniques, and methodological categories, we offer detailed insights into the area of text classification. The goal of this survey is to assist scholars in choosing the right methods for specific projects while encouraging further advancements in this area. This detailed examination not only contributes to the scholarly conversation on text classification but also seeks to direct future progress by identifying promising avenues for innovation and enhancement. The primary contributions of this article include the sophisticated methodological classification, a thorough review and examination of state-of-the-art algorithms, along with observational and experimental assessments, and a visionary outlook on the future development of text classification methods. |
| format | Article |
| id | doaj-art-72e8c8c2e17e47509eaebff2da8e2327 |
| institution | Matheson Library |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-72e8c8c2e17e47509eaebff2da8e23272025-07-25T08:09:36ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-05-018362466010.26599/BDMA.2024.9020092Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental InvestigationKamal Taha0Paul D. Yoo1Chan Yeun2Aya Taha3Department of Computer Science, Khalifa University, Abu Dhabi 127788, United Arab EmiratesSchool of Computing and Mathematical Sciences, Birkbeck College, University of London, London, WC1E 7HU, UKDepartment of Computer Science, Khalifa University, Abu Dhabi 127788, United Arab EmiratesDepartment of Science, Brighton College, Dubai 122002, United Arab EmiratesThis review article provides a thorough assessment of modern and innovative algorithms for text classification through both observational and experimental evaluations. We propose a new classification system, grounded in methodology, to categorize text classification algorithms into an organized structure from general categories down to particular fine-grained techniques. Drawing on more than 100 academic papers from prominent publishers, our extensive review spans a wide range of algorithms, encompassing traditional, deep learning, and emerging approaches. Through observational studies and comparative experiments among various algorithms, techniques, and methodological categories, we offer detailed insights into the area of text classification. The goal of this survey is to assist scholars in choosing the right methods for specific projects while encouraging further advancements in this area. This detailed examination not only contributes to the scholarly conversation on text classification but also seeks to direct future progress by identifying promising avenues for innovation and enhancement. The primary contributions of this article include the sophisticated methodological classification, a thorough review and examination of state-of-the-art algorithms, along with observational and experimental assessments, and a visionary outlook on the future development of text classification methods.https://www.sciopen.com/article/10.26599/BDMA.2024.9020092text classificationclassical methodsdeep learning methodsexperimental evaluation |
| spellingShingle | Kamal Taha Paul D. Yoo Chan Yeun Aya Taha Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation Big Data Mining and Analytics text classification classical methods deep learning methods experimental evaluation |
| title | Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation |
| title_full | Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation |
| title_fullStr | Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation |
| title_full_unstemmed | Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation |
| title_short | Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation |
| title_sort | text classification techniques a holistic review observational analysis and experimental investigation |
| topic | text classification classical methods deep learning methods experimental evaluation |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020092 |
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