Text Classification: How Machine Learning Is Revolutionizing Text Categorization
The automated classification of texts into predefined categories has become increasingly prominent, driven by the exponential growth of digital documents and the demand for efficient organization. This paper serves as an in-depth survey of text classification and machine learning, consolidating dive...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/16/2/130 |
| _version_ | 1826582339639050240 |
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| author | Hesham Allam Lisa Makubvure Benjamin Gyamfi Kwadwo Nyarko Graham Kehinde Akinwolere |
| author_facet | Hesham Allam Lisa Makubvure Benjamin Gyamfi Kwadwo Nyarko Graham Kehinde Akinwolere |
| author_sort | Hesham Allam |
| collection | DOAJ |
| description | The automated classification of texts into predefined categories has become increasingly prominent, driven by the exponential growth of digital documents and the demand for efficient organization. This paper serves as an in-depth survey of text classification and machine learning, consolidating diverse aspects of the field into a single, comprehensive resource—a rarity in the current body of literature. Few studies have achieved such breadth, and this work aims to provide a unified perspective, offering a significant contribution to researchers and the academic community. The survey examines the evolution of machine learning in text categorization (TC), highlighting its transformative advantages over manual classification, such as enhanced accuracy, reduced labor, and adaptability across domains. It delves into various TC tasks and contrasts machine learning methodologies with knowledge engineering approaches, demonstrating the strengths and flexibility of data-driven techniques. Key applications of TC are explored, alongside an analysis of critical machine learning methods, including document representation techniques and dimensionality reduction strategies. Moreover, this study evaluates a range of text categorization models, identifies persistent challenges like class imbalance and overfitting, and investigates emerging trends shaping the future of the field. It discusses essential components such as document representation, classifier construction, and performance evaluation, offering a well-rounded understanding of the current state of TC. Importantly, this paper also provides clear research directions, emphasizing areas requiring further innovation, such as hybrid methodologies, explainable AI (XAI), and scalable approaches for low-resource languages. By bridging gaps in existing knowledge and suggesting actionable paths forward, this work positions itself as a vital resource for academics and industry practitioners, fostering deeper exploration and development in text classification. |
| first_indexed | 2025-03-14T15:04:24Z |
| format | Article |
| id | doaj.art-ec4b48ea342041319c873d56b465416e |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2025-03-14T15:04:24Z |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ec4b48ea342041319c873d56b465416e2025-02-25T13:31:42ZengMDPI AGInformation2078-24892025-02-0116213010.3390/info16020130Text Classification: How Machine Learning Is Revolutionizing Text CategorizationHesham Allam0Lisa Makubvure1Benjamin Gyamfi2Kwadwo Nyarko Graham3Kehinde Akinwolere4Center for Information & Communication Sciences (CICS), Ball State University, Muncie, IN 47306, USACenter for Information & Communication Sciences (CICS), Ball State University, Muncie, IN 47306, USACenter for Information & Communication Sciences (CICS), Ball State University, Muncie, IN 47306, USACenter for Information & Communication Sciences (CICS), Ball State University, Muncie, IN 47306, USACenter for Information & Communication Sciences (CICS), Ball State University, Muncie, IN 47306, USAThe automated classification of texts into predefined categories has become increasingly prominent, driven by the exponential growth of digital documents and the demand for efficient organization. This paper serves as an in-depth survey of text classification and machine learning, consolidating diverse aspects of the field into a single, comprehensive resource—a rarity in the current body of literature. Few studies have achieved such breadth, and this work aims to provide a unified perspective, offering a significant contribution to researchers and the academic community. The survey examines the evolution of machine learning in text categorization (TC), highlighting its transformative advantages over manual classification, such as enhanced accuracy, reduced labor, and adaptability across domains. It delves into various TC tasks and contrasts machine learning methodologies with knowledge engineering approaches, demonstrating the strengths and flexibility of data-driven techniques. Key applications of TC are explored, alongside an analysis of critical machine learning methods, including document representation techniques and dimensionality reduction strategies. Moreover, this study evaluates a range of text categorization models, identifies persistent challenges like class imbalance and overfitting, and investigates emerging trends shaping the future of the field. It discusses essential components such as document representation, classifier construction, and performance evaluation, offering a well-rounded understanding of the current state of TC. Importantly, this paper also provides clear research directions, emphasizing areas requiring further innovation, such as hybrid methodologies, explainable AI (XAI), and scalable approaches for low-resource languages. By bridging gaps in existing knowledge and suggesting actionable paths forward, this work positions itself as a vital resource for academics and industry practitioners, fostering deeper exploration and development in text classification.https://www.mdpi.com/2078-2489/16/2/130text categorization (TC)machine learningautomationdocument representationdimension reductionclassifier evaluation |
| spellingShingle | Hesham Allam Lisa Makubvure Benjamin Gyamfi Kwadwo Nyarko Graham Kehinde Akinwolere Text Classification: How Machine Learning Is Revolutionizing Text Categorization Information text categorization (TC) machine learning automation document representation dimension reduction classifier evaluation |
| title | Text Classification: How Machine Learning Is Revolutionizing Text Categorization |
| title_full | Text Classification: How Machine Learning Is Revolutionizing Text Categorization |
| title_fullStr | Text Classification: How Machine Learning Is Revolutionizing Text Categorization |
| title_full_unstemmed | Text Classification: How Machine Learning Is Revolutionizing Text Categorization |
| title_short | Text Classification: How Machine Learning Is Revolutionizing Text Categorization |
| title_sort | text classification how machine learning is revolutionizing text categorization |
| topic | text categorization (TC) machine learning automation document representation dimension reduction classifier evaluation |
| url | https://www.mdpi.com/2078-2489/16/2/130 |
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