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@article{202144,
        author = {Dr. Kavita Yogesh Dhakad and Dr. Dipali Meher},
        title = {Exploring Text Classification Algorithms: A Systematic Literature Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8914-8920},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202144},
        abstract = {Text classification is an essential task in natural language processing with wide-ranging applications, including sentiment analysis, spams filtering, and document categorization. This study aims to investigate diverse text classification algorithms and assess their ability to accurately assign predefined categories to text documents. This Systematic Literature Review commences by providing an overview of various text classification techniques. In this research 31 research articles are considered. This includes traditional machine learning approaches such as Naive Bayes, Support Vector Machines (SVM), Decision Trees (DT) and Random Forest (RF) along with more advanced methods like deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The study seeks to gain insights into their effectiveness for text classification tasks. It explores how each algorithm handles different types of data and the challenges they may encounter. The author will also identify application area of these algorithms. Understanding the performance characteristics of these algorithms is crucial for selecting the most appropriate approach for specific text classification scenarios. This study will give future directions to apply text classification in syllabus classification on large data.},
        keywords = {Text Classification, Text Classification Algorithm, Naive Bayes, Support Vector Machines, Deep Learning},
        month = {May},
        }