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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2096-0654 2097-406X |