A REVIEW OF TEXT CLASSIFICATION TECHNIQUES ON LEARNING ALGORITHMS 
DOI: https://doi.org/10.65725/JCISE/2/2/009
JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)
ISSN: 3107-8168
Volume 2 Issue 2, Apr-June 2026

Abstract: 
Text classification plays a strong role in organizing and receiving meaningful information from huge collections of both unstructured and structured data. Considering the immense progress of digital contents across the domains, the manual categorization has become unreal-istic, paving the way for automated classification techniques generated by data mining, natu-ral language processing (NLP), and machine learning. Traditional approaches depend heavily on handcrafted features and classical classifiers, but recent advancements in deep learning and graph-based methods have significantly enhanced performance by activating automatic feature extraction and better contextual representation results for the text. This paper provides a comprehensive review of document classification methods, tracking the evolution from rule-based and statistical models the importance of preprocessing, feature engineering, and dataset availability. Moreover, the study identifies unresolved research challenges related to classifi-cation effectiveness, model comprehensibility, and scalability of the proposed approaches.

Authors: Mr.A. Ravi and Dr R.Velmurugan 

Keywords: Natural language processing, preprocessing, Text classification.